USCensus1977:HW4: Life.Exp regression
bdanalytics
Date: (Sun) Apr 26, 2015
Introduction:
Data: Source: Training: https://courses.edx.org/c4x/MITx/15.071x_2/asset/statedataSimple.csv
New:
Time period:
Synopsis:
Based on analysis utilizing <> techniques,
[](.png)
Potential next steps include:
- Organization:
- Categorize by chunk
- Priority criteria:
- Ease of change
- Impacts report
- Cleans innards
- Bug report
- manage.missing.data chunk:
- cleaner way to manage re-splitting of training vs. new entity
- fit.models chunk:
- Prediction accuracy scatter graph:
- Add tiles (raw vs. PCA)
- Use shiny for drop-down of “important” features
Use plot.ly for interactive plots ?
- Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
- move model_type parameter to myfit_mdl before indep_vars_vctr (keep all model_* together)
- create a custom model for rpart that has minbucket as a tuning parameter
varImp for randomForest crashes in caret version:6.0.41 -> submit bug report
- Probability handling for multinomials vs. desired binomial outcome
- ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
extensions toward multiclass classification are scheduled for the next release
- Skip trControl.method=“cv” for dummy classifier ?
- Add custom model to caret for a dummy (baseline) classifier (binomial & multinomial) that generates proba/outcomes which mimics the freq distribution of glb_rsp_var values; Right now glb_dmy_glm_mdl always generates most frequent outcome in training data
glm_dmy_mdl should use the same method as glm_sel_mdl until custom dummy classifer is implemented
- Compare glb_sel_mdl vs. glb_fin_mdl:
- varImp
- Prediction differences (shd be minimal ?)
- Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
- Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
- Parameterize glb_analytics_pn
- Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
- Replicate myfit_mdl_classification features in myfit_mdl_regression
- Do non-glm methods handle interaction terms ?
- f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
- Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
- Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
- Incorporate code chunks in print_sessionInfo
- Test against
- projects in github.com/bdanalytics
- lectures in jhu-datascience track
Analysis:
rm(list=ls())
set.seed(12345)
options(stringsAsFactors=FALSE)
source("~/Dropbox/datascience/R/mydsutils.R")
source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/mypetrinet.R")
# Gather all package requirements here
#suppressPackageStartupMessages(require())
#packageVersion("snow")
#require(sos); findFn("pinv", maxPages=2, sortby="MaxScore")
# Analysis control global variables
glb_trnng_url <- "https://courses.edx.org/c4x/MITx/15.071x_2/asset/statedata.csv"
glb_newdt_url <- "<newdt_url>"
glb_is_separate_newent_dataset <- FALSE # or TRUE
glb_split_entity_newent_datasets <- TRUE # or FALSE
glb_split_newdata_method <- "copy" # "condition" or "sample" or "copy"
glb_split_newdata_condition <- "<col_name> <condition_operator> <value>" # or NULL
glb_split_newdata_size_ratio <- 0.1 # > 0 & < 1
glb_split_sample.seed <- 88 # or any integer
glb_max_obs <- NULL # or any integer
glb_is_regression <- TRUE; glb_is_classification <- FALSE; glb_is_binomial <- FALSE
glb_rsp_var_raw <- "Life.Exp"
# for classification, the response variable has to be a factor
glb_rsp_var <- glb_rsp_var_raw # or "Life.Exp.fctr"
# if the response factor is based on numbers e.g (0/1 vs. "A"/"B"),
# caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- NULL # or function(raw) {
#relevel(factor(ifelse(raw == 1, "R", "A")), as.factor(c("R", "A")), ref="A")
#as.factor(paste0("B", raw))
#as.factor(raw)
#}
#glb_map_rsp_raw_to_var(c(1, 2, 3, 4, 5))
glb_map_rsp_var_to_raw <- NULL # or function(var) {
#as.numeric(var) - 1
#as.numeric(var)
#levels(var)[as.numeric(var)]
#c(" <=50K", " >50K")[as.numeric(var)]
#}
#glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(c(1, 2, 3, 4, 5)))
if ((glb_rsp_var != glb_rsp_var_raw) & is.null(glb_map_rsp_raw_to_var))
stop("glb_map_rsp_raw_to_var function expected")
glb_rsp_var_out <- paste0(glb_rsp_var, ".predict.") # model_id is appended later
glb_id_vars <- c("state.abb")
# List transformed vars
glb_exclude_vars_as_features <- c("state.abb", "state.division", "state.region", "state.area")
# List feats that shd be excluded due to known causation by prediction variable
if (glb_rsp_var_raw != glb_rsp_var)
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
glb_rsp_var_raw)
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
c("state.name")) # or c("<col_name>")
# List output vars (useful during testing in console)
# glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
# grep(glb_rsp_var_out, names(glb_trnent_df), value=TRUE))
glb_impute_na_data <- FALSE # or TRUE
glb_mice_complete.seed <- 144 # or any integer
# rpart: .rnorm messes with the models badly
# caret creates dummy vars for factor feats which messes up the tuning
# - better to feed as.numeric(<feat>.fctr) to caret
# Regression
if (glb_is_regression)
glb_models_method_vctr <- c("lm", "glm", "rpart", "rf") else
# Classification
if (glb_is_binomial)
glb_models_method_vctr <- c("glm", "rpart", "rf") else
glb_models_method_vctr <- c("rpart", "rf")
glb_models_lst <- list(); glb_models_df <- data.frame()
# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<col_name>")
glb_model_metric_terms <- NULL # or matrix(c(
# 0,1,2,3,4,
# 2,0,1,2,3,
# 4,2,0,1,2,
# 6,4,2,0,1,
# 8,6,4,2,0
# ), byrow=TRUE, nrow=5)
glb_model_metric <- NULL # or "<metric_name>"
glb_model_metric_maximize <- NULL # or FALSE (TRUE is not the default for both classification & regression)
glb_model_metric_smmry <- NULL # or function(data, lev=NULL, model=NULL) {
# confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
# #print(confusion_mtrx)
# #print(confusion_mtrx * glb_model_metric_terms)
# metric <- sum(confusion_mtrx * glb_model_metric_terms) / nrow(data)
# names(metric) <- glb_model_metric
# return(metric)
# }
glb_tune_models_df <-
rbind(
#data.frame(parameter="cp", min=0.00005, max=0.00005, by=0.000005),
#seq(from=0.01, to=0.01, by=0.01)
#data.frame(parameter="mtry", min=2, max=4, by=1),
data.frame(parameter="dummy", min=2, max=4, by=1)
)
# or NULL
glb_n_cv_folds <- 3 # or NULL
glb_clf_proba_threshold <- NULL # 0.5
# Model selection criteria
if (glb_is_regression)
glb_model_evl_criteria <- c("min.RMSE.OOB", "max.R.sq.OOB", "max.Adj.R.sq.fit")
if (glb_is_classification) {
if (glb_is_binomial)
glb_model_evl_criteria <- c("max.Accuracy.OOB", "max.Kappa.OOB", "min.aic.fit") else
glb_model_evl_criteria <- c("max.Accuracy.OOB", "max.Kappa.OOB")
}
glb_sel_mdl_id <- NULL # or "<model_id_prefix>.<model_method>"
glb_fin_mdl_id <- glb_sel_mdl_id # or "Final"
glb_out_pfx <- "USCensus1977_State_HW4_"
# Depict process
glb_analytics_pn <- petrinet(name="glb_analytics_pn",
trans_df=data.frame(id=1:6,
name=c("data.training.all","data.new",
"model.selected","model.final",
"data.training.all.prediction","data.new.prediction"),
x=c( -5,-5,-15,-25,-25,-35),
y=c( -5, 5, 0, 0, -5, 5)
),
places_df=data.frame(id=1:4,
name=c("bgn","fit.data.training.all","predict.data.new","end"),
x=c( -0, -20, -30, -40),
y=c( 0, 0, 0, 0),
M0=c( 3, 0, 0, 0)
),
arcs_df=data.frame(
begin=c("bgn","bgn","bgn",
"data.training.all","model.selected","fit.data.training.all",
"fit.data.training.all","model.final",
"data.new","predict.data.new",
"data.training.all.prediction","data.new.prediction"),
end =c("data.training.all","data.new","model.selected",
"fit.data.training.all","fit.data.training.all","model.final",
"data.training.all.prediction","predict.data.new",
"predict.data.new","data.new.prediction",
"end","end")
))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())## Loading required package: grid
glb_analytics_avl_objs <- NULL
glb_script_tm <- proc.time()
glb_script_df <- data.frame(chunk_label="import_data",
chunk_step_major=1, chunk_step_minor=0,
elapsed=(proc.time() - glb_script_tm)["elapsed"])
print(tail(glb_script_df, 2))## chunk_label chunk_step_major chunk_step_minor elapsed
## elapsed import_data 1 0 0.002Step 1: import data
glb_entity_df <- myimport_data(url=glb_trnng_url,
comment=ifelse(!glb_is_separate_newent_dataset, "glb_entity_df", "glb_trnent_df"),
force_header=TRUE)## [1] "Reading file ./data/statedata.csv..."
## [1] "dimensions of data in ./data/statedata.csv: 50 rows x 15 cols"
## Population Income Illiteracy Life.Exp Murder HS.Grad Frost Area
## 1 3615 3624 2.1 69.05 15.1 41.3 20 50708
## 2 365 6315 1.5 69.31 11.3 66.7 152 566432
## 3 2212 4530 1.8 70.55 7.8 58.1 15 113417
## 4 2110 3378 1.9 70.66 10.1 39.9 65 51945
## 5 21198 5114 1.1 71.71 10.3 62.6 20 156361
## 6 2541 4884 0.7 72.06 6.8 63.9 166 103766
## state.abb state.area x y state.division state.name
## 1 AL 51609 -86.7509 32.5901 East South Central Alabama
## 2 AK 589757 -127.2500 49.2500 Pacific Alaska
## 3 AZ 113909 -111.6250 34.2192 Mountain Arizona
## 4 AR 53104 -92.2992 34.7336 West South Central Arkansas
## 5 CA 158693 -119.7730 36.5341 Pacific California
## 6 CO 104247 -105.5130 38.6777 Mountain Colorado
## state.region
## 1 South
## 2 West
## 3 West
## 4 South
## 5 West
## 6 West
## Population Income Illiteracy Life.Exp Murder HS.Grad Frost Area
## 2 365 6315 1.5 69.31 11.3 66.7 152 566432
## 7 3100 5348 1.1 72.48 3.1 56.0 139 4862
## 17 3387 3712 1.6 70.10 10.6 38.5 95 39650
## 25 4767 4254 0.8 70.69 9.3 48.8 108 68995
## 35 10735 4561 0.8 70.82 7.4 53.2 124 40975
## 47 3559 4864 0.6 71.72 4.3 63.5 32 66570
## state.abb state.area x y state.division state.name
## 2 AK 589757 -127.2500 49.2500 Pacific Alaska
## 7 CT 5009 -72.3573 41.5928 New England Connecticut
## 17 KY 40395 -84.7674 37.3915 East South Central Kentucky
## 25 MO 69686 -92.5137 38.3347 West North Central Missouri
## 35 OH 41222 -82.5963 40.2210 East North Central Ohio
## 47 WA 68192 -119.7460 47.4231 Pacific Washington
## state.region
## 2 West
## 7 Northeast
## 17 South
## 25 North Central
## 35 North Central
## 47 West
## Population Income Illiteracy Life.Exp Murder HS.Grad Frost Area
## 45 472 3907 0.6 71.64 5.5 57.1 168 9267
## 46 4981 4701 1.4 70.08 9.5 47.8 85 39780
## 47 3559 4864 0.6 71.72 4.3 63.5 32 66570
## 48 1799 3617 1.4 69.48 6.7 41.6 100 24070
## 49 4589 4468 0.7 72.48 3.0 54.5 149 54464
## 50 376 4566 0.6 70.29 6.9 62.9 173 97203
## state.abb state.area x y state.division state.name
## 45 VT 9609 -72.5450 44.2508 New England Vermont
## 46 VA 40815 -78.2005 37.5630 South Atlantic Virginia
## 47 WA 68192 -119.7460 47.4231 Pacific Washington
## 48 WV 24181 -80.6665 38.4204 South Atlantic West Virginia
## 49 WI 56154 -89.9941 44.5937 East North Central Wisconsin
## 50 WY 97914 -107.2560 43.0504 Mountain Wyoming
## state.region
## 45 Northeast
## 46 South
## 47 West
## 48 South
## 49 North Central
## 50 West
## 'data.frame': 50 obs. of 15 variables:
## $ Population : int 3615 365 2212 2110 21198 2541 3100 579 8277 4931 ...
## $ Income : int 3624 6315 4530 3378 5114 4884 5348 4809 4815 4091 ...
## $ Illiteracy : num 2.1 1.5 1.8 1.9 1.1 0.7 1.1 0.9 1.3 2 ...
## $ Life.Exp : num 69 69.3 70.5 70.7 71.7 ...
## $ Murder : num 15.1 11.3 7.8 10.1 10.3 6.8 3.1 6.2 10.7 13.9 ...
## $ HS.Grad : num 41.3 66.7 58.1 39.9 62.6 63.9 56 54.6 52.6 40.6 ...
## $ Frost : int 20 152 15 65 20 166 139 103 11 60 ...
## $ Area : int 50708 566432 113417 51945 156361 103766 4862 1982 54090 58073 ...
## $ state.abb : chr "AL" "AK" "AZ" "AR" ...
## $ state.area : int 51609 589757 113909 53104 158693 104247 5009 2057 58560 58876 ...
## $ x : num -86.8 -127.2 -111.6 -92.3 -119.8 ...
## $ y : num 32.6 49.2 34.2 34.7 36.5 ...
## $ state.division: chr "East South Central" "Pacific" "Mountain" "West South Central" ...
## $ state.name : chr "Alabama" "Alaska" "Arizona" "Arkansas" ...
## $ state.region : chr "South" "West" "West" "South" ...
## - attr(*, "comment")= chr "glb_entity_df"
## NULLif (!glb_is_separate_newent_dataset) {
glb_trnent_df <- glb_entity_df; comment(glb_trnent_df) <- "glb_trnent_df"
} # else glb_entity_df is maintained as is for chunk:inspectORexplore.data
if (glb_is_separate_newent_dataset) {
glb_newent_df <- myimport_data(
url=glb_newdt_url,
comment="glb_newent_df", force_header=TRUE)
# To make plots / stats / checks easier in chunk:inspectORexplore.data
glb_entity_df <- rbind(glb_trnent_df, glb_newent_df); comment(glb_entity_df) <- "glb_entity_df"
} else {
if (!glb_split_entity_newent_datasets) {
stop("Not implemented yet")
glb_newent_df <- glb_trnent_df[sample(1:nrow(glb_trnent_df),
max(2, nrow(glb_trnent_df) / 1000)),]
} else if (glb_split_newdata_method == "condition") {
glb_newent_df <- do.call("subset",
list(glb_trnent_df, parse(text=glb_split_newdata_condition)))
glb_trnent_df <- do.call("subset",
list(glb_trnent_df, parse(text=paste0("!(",
glb_split_newdata_condition,
")"))))
} else if (glb_split_newdata_method == "sample") {
require(caTools)
set.seed(glb_split_sample.seed)
split <- sample.split(glb_trnent_df[, glb_rsp_var_raw],
SplitRatio=(1-glb_split_newdata_size_ratio))
glb_newent_df <- glb_trnent_df[!split, ]
glb_trnent_df <- glb_trnent_df[split ,]
} else if (glb_split_newdata_method == "copy") {
glb_trnent_df <- glb_entity_df
comment(glb_trnent_df) <- "glb_trnent_df"
glb_newent_df <- glb_entity_df
comment(glb_newent_df) <- "glb_newent_df"
} else stop("glb_split_newdata_method should be %in% c('condition', 'sample', 'copy')")
comment(glb_newent_df) <- "glb_newent_df"
myprint_df(glb_newent_df)
str(glb_newent_df)
if (glb_split_entity_newent_datasets) {
myprint_df(glb_trnent_df)
str(glb_trnent_df)
}
} ## Population Income Illiteracy Life.Exp Murder HS.Grad Frost Area
## 1 3615 3624 2.1 69.05 15.1 41.3 20 50708
## 2 365 6315 1.5 69.31 11.3 66.7 152 566432
## 3 2212 4530 1.8 70.55 7.8 58.1 15 113417
## 4 2110 3378 1.9 70.66 10.1 39.9 65 51945
## 5 21198 5114 1.1 71.71 10.3 62.6 20 156361
## 6 2541 4884 0.7 72.06 6.8 63.9 166 103766
## state.abb state.area x y state.division state.name
## 1 AL 51609 -86.7509 32.5901 East South Central Alabama
## 2 AK 589757 -127.2500 49.2500 Pacific Alaska
## 3 AZ 113909 -111.6250 34.2192 Mountain Arizona
## 4 AR 53104 -92.2992 34.7336 West South Central Arkansas
## 5 CA 158693 -119.7730 36.5341 Pacific California
## 6 CO 104247 -105.5130 38.6777 Mountain Colorado
## state.region
## 1 South
## 2 West
## 3 West
## 4 South
## 5 West
## 6 West
## Population Income Illiteracy Life.Exp Murder HS.Grad Frost Area
## 1 3615 3624 2.1 69.05 15.1 41.3 20 50708
## 18 3806 3545 2.8 68.76 13.2 42.2 12 44930
## 19 1058 3694 0.7 70.39 2.7 54.7 161 30920
## 22 9111 4751 0.9 70.63 11.1 52.8 125 56817
## 37 2284 4660 0.6 72.13 4.2 60.0 44 96184
## 48 1799 3617 1.4 69.48 6.7 41.6 100 24070
## state.abb state.area x y state.division state.name
## 1 AL 51609 -86.7509 32.5901 East South Central Alabama
## 18 LA 48523 -92.2724 30.6181 West South Central Louisiana
## 19 ME 33215 -68.9801 45.6226 New England Maine
## 22 MI 58216 -84.6870 43.1361 East North Central Michigan
## 37 OR 96981 -120.0680 43.9078 Pacific Oregon
## 48 WV 24181 -80.6665 38.4204 South Atlantic West Virginia
## state.region
## 1 South
## 18 South
## 19 Northeast
## 22 North Central
## 37 West
## 48 South
## Population Income Illiteracy Life.Exp Murder HS.Grad Frost Area
## 45 472 3907 0.6 71.64 5.5 57.1 168 9267
## 46 4981 4701 1.4 70.08 9.5 47.8 85 39780
## 47 3559 4864 0.6 71.72 4.3 63.5 32 66570
## 48 1799 3617 1.4 69.48 6.7 41.6 100 24070
## 49 4589 4468 0.7 72.48 3.0 54.5 149 54464
## 50 376 4566 0.6 70.29 6.9 62.9 173 97203
## state.abb state.area x y state.division state.name
## 45 VT 9609 -72.5450 44.2508 New England Vermont
## 46 VA 40815 -78.2005 37.5630 South Atlantic Virginia
## 47 WA 68192 -119.7460 47.4231 Pacific Washington
## 48 WV 24181 -80.6665 38.4204 South Atlantic West Virginia
## 49 WI 56154 -89.9941 44.5937 East North Central Wisconsin
## 50 WY 97914 -107.2560 43.0504 Mountain Wyoming
## state.region
## 45 Northeast
## 46 South
## 47 West
## 48 South
## 49 North Central
## 50 West
## 'data.frame': 50 obs. of 15 variables:
## $ Population : int 3615 365 2212 2110 21198 2541 3100 579 8277 4931 ...
## $ Income : int 3624 6315 4530 3378 5114 4884 5348 4809 4815 4091 ...
## $ Illiteracy : num 2.1 1.5 1.8 1.9 1.1 0.7 1.1 0.9 1.3 2 ...
## $ Life.Exp : num 69 69.3 70.5 70.7 71.7 ...
## $ Murder : num 15.1 11.3 7.8 10.1 10.3 6.8 3.1 6.2 10.7 13.9 ...
## $ HS.Grad : num 41.3 66.7 58.1 39.9 62.6 63.9 56 54.6 52.6 40.6 ...
## $ Frost : int 20 152 15 65 20 166 139 103 11 60 ...
## $ Area : int 50708 566432 113417 51945 156361 103766 4862 1982 54090 58073 ...
## $ state.abb : chr "AL" "AK" "AZ" "AR" ...
## $ state.area : int 51609 589757 113909 53104 158693 104247 5009 2057 58560 58876 ...
## $ x : num -86.8 -127.2 -111.6 -92.3 -119.8 ...
## $ y : num 32.6 49.2 34.2 34.7 36.5 ...
## $ state.division: chr "East South Central" "Pacific" "Mountain" "West South Central" ...
## $ state.name : chr "Alabama" "Alaska" "Arizona" "Arkansas" ...
## $ state.region : chr "South" "West" "West" "South" ...
## - attr(*, "comment")= chr "glb_newent_df"
## Population Income Illiteracy Life.Exp Murder HS.Grad Frost Area
## 1 3615 3624 2.1 69.05 15.1 41.3 20 50708
## 2 365 6315 1.5 69.31 11.3 66.7 152 566432
## 3 2212 4530 1.8 70.55 7.8 58.1 15 113417
## 4 2110 3378 1.9 70.66 10.1 39.9 65 51945
## 5 21198 5114 1.1 71.71 10.3 62.6 20 156361
## 6 2541 4884 0.7 72.06 6.8 63.9 166 103766
## state.abb state.area x y state.division state.name
## 1 AL 51609 -86.7509 32.5901 East South Central Alabama
## 2 AK 589757 -127.2500 49.2500 Pacific Alaska
## 3 AZ 113909 -111.6250 34.2192 Mountain Arizona
## 4 AR 53104 -92.2992 34.7336 West South Central Arkansas
## 5 CA 158693 -119.7730 36.5341 Pacific California
## 6 CO 104247 -105.5130 38.6777 Mountain Colorado
## state.region
## 1 South
## 2 West
## 3 West
## 4 South
## 5 West
## 6 West
## Population Income Illiteracy Life.Exp Murder HS.Grad Frost Area
## 9 8277 4815 1.3 70.66 10.7 52.6 11 54090
## 16 2280 4669 0.6 72.58 4.5 59.9 114 81787
## 22 9111 4751 0.9 70.63 11.1 52.8 125 56817
## 32 18076 4903 1.4 70.55 10.9 52.7 82 47831
## 45 472 3907 0.6 71.64 5.5 57.1 168 9267
## 47 3559 4864 0.6 71.72 4.3 63.5 32 66570
## state.abb state.area x y state.division state.name
## 9 FL 58560 -81.6850 27.8744 South Atlantic Florida
## 16 KS 82264 -98.1156 38.4204 West North Central Kansas
## 22 MI 58216 -84.6870 43.1361 East North Central Michigan
## 32 NY 49576 -75.1449 43.1361 Middle Atlantic New York
## 45 VT 9609 -72.5450 44.2508 New England Vermont
## 47 WA 68192 -119.7460 47.4231 Pacific Washington
## state.region
## 9 South
## 16 North Central
## 22 North Central
## 32 Northeast
## 45 Northeast
## 47 West
## Population Income Illiteracy Life.Exp Murder HS.Grad Frost Area
## 45 472 3907 0.6 71.64 5.5 57.1 168 9267
## 46 4981 4701 1.4 70.08 9.5 47.8 85 39780
## 47 3559 4864 0.6 71.72 4.3 63.5 32 66570
## 48 1799 3617 1.4 69.48 6.7 41.6 100 24070
## 49 4589 4468 0.7 72.48 3.0 54.5 149 54464
## 50 376 4566 0.6 70.29 6.9 62.9 173 97203
## state.abb state.area x y state.division state.name
## 45 VT 9609 -72.5450 44.2508 New England Vermont
## 46 VA 40815 -78.2005 37.5630 South Atlantic Virginia
## 47 WA 68192 -119.7460 47.4231 Pacific Washington
## 48 WV 24181 -80.6665 38.4204 South Atlantic West Virginia
## 49 WI 56154 -89.9941 44.5937 East North Central Wisconsin
## 50 WY 97914 -107.2560 43.0504 Mountain Wyoming
## state.region
## 45 Northeast
## 46 South
## 47 West
## 48 South
## 49 North Central
## 50 West
## 'data.frame': 50 obs. of 15 variables:
## $ Population : int 3615 365 2212 2110 21198 2541 3100 579 8277 4931 ...
## $ Income : int 3624 6315 4530 3378 5114 4884 5348 4809 4815 4091 ...
## $ Illiteracy : num 2.1 1.5 1.8 1.9 1.1 0.7 1.1 0.9 1.3 2 ...
## $ Life.Exp : num 69 69.3 70.5 70.7 71.7 ...
## $ Murder : num 15.1 11.3 7.8 10.1 10.3 6.8 3.1 6.2 10.7 13.9 ...
## $ HS.Grad : num 41.3 66.7 58.1 39.9 62.6 63.9 56 54.6 52.6 40.6 ...
## $ Frost : int 20 152 15 65 20 166 139 103 11 60 ...
## $ Area : int 50708 566432 113417 51945 156361 103766 4862 1982 54090 58073 ...
## $ state.abb : chr "AL" "AK" "AZ" "AR" ...
## $ state.area : int 51609 589757 113909 53104 158693 104247 5009 2057 58560 58876 ...
## $ x : num -86.8 -127.2 -111.6 -92.3 -119.8 ...
## $ y : num 32.6 49.2 34.2 34.7 36.5 ...
## $ state.division: chr "East South Central" "Pacific" "Mountain" "West South Central" ...
## $ state.name : chr "Alabama" "Alaska" "Arizona" "Arkansas" ...
## $ state.region : chr "South" "West" "West" "South" ...
## - attr(*, "comment")= chr "glb_trnent_df"if (!is.null(glb_max_obs)) {
if (nrow(glb_trnent_df) > glb_max_obs) {
warning("glb_trnent_df restricted to glb_max_obs: ", format(glb_max_obs, big.mark=","))
org_entity_df <- glb_trnent_df
glb_trnent_df <- org_entity_df[split <-
sample.split(org_entity_df[, glb_rsp_var_raw], SplitRatio=glb_max_obs), ]
org_entity_df <- NULL
}
# if (nrow(glb_newent_df) > glb_max_obs) {
# warning("glb_newent_df restricted to glb_max_obs: ", format(glb_max_obs, big.mark=","))
# org_newent_df <- glb_newent_df
# glb_newent_df <- org_newent_df[split <-
# sample.split(org_newent_df[, glb_rsp_var_raw], SplitRatio=glb_max_obs), ]
# org_newent_df <- NULL
# }
}
if (nrow(glb_trnent_df) == nrow(glb_entity_df))
warning("glb_trnent_df same as glb_entity_df")## Warning: glb_trnent_df same as glb_entity_dfif (nrow(glb_newent_df) == nrow(glb_entity_df))
warning("glb_newent_df same as glb_entity_df")## Warning: glb_newent_df same as glb_entity_dfglb_script_df <- rbind(glb_script_df,
data.frame(chunk_label="cleanse_data",
chunk_step_major=max(glb_script_df$chunk_step_major)+1,
chunk_step_minor=0,
elapsed=(proc.time() - glb_script_tm)["elapsed"]))
print(tail(glb_script_df, 2))## chunk_label chunk_step_major chunk_step_minor elapsed
## elapsed import_data 1 0 0.002
## elapsed1 cleanse_data 2 0 0.413Step 2: cleanse data
glb_script_df <- rbind(glb_script_df,
data.frame(chunk_label="inspectORexplore.data",
chunk_step_major=max(glb_script_df$chunk_step_major),
chunk_step_minor=1,
elapsed=(proc.time() - glb_script_tm)["elapsed"]))
print(tail(glb_script_df, 2))## chunk_label chunk_step_major chunk_step_minor elapsed
## elapsed1 cleanse_data 2 0 0.413
## elapsed2 inspectORexplore.data 2 1 0.449Step 2.1: inspect/explore data
#print(str(glb_trnent_df))
#View(glb_trnent_df)
# List info gathered for various columns
# <col_name>: <description>; <notes>
# Create new features that help diagnostics
# Create factors of string variables
str_vars <- sapply(1:length(names(glb_trnent_df)),
function(col) ifelse(class(glb_trnent_df[, names(glb_trnent_df)[col]]) == "character",
names(glb_trnent_df)[col], ""))
if (length(str_vars <- setdiff(str_vars[str_vars != ""],
glb_exclude_vars_as_features)) > 0) {
warning("Creating factors of string variables:", paste0(str_vars, collapse=", "))
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, str_vars)
for (var in str_vars) {
glb_entity_df[, paste0(var, ".fctr")] <- factor(glb_entity_df[, var],
as.factor(unique(glb_entity_df[, var])))
glb_trnent_df[, paste0(var, ".fctr")] <- factor(glb_trnent_df[, var],
as.factor(unique(glb_entity_df[, var])))
glb_newent_df[, paste0(var, ".fctr")] <- factor(glb_newent_df[, var],
as.factor(unique(glb_entity_df[, var])))
}
}
# Convert factors to dummy variables
# Build splines require(splines); bsBasis <- bs(training$age, df=3)
add_new_diag_feats <- function(obs_df, ref_df=glb_entity_df) {
require(plyr)
obs_df <- mutate(obs_df,
# <col_name>.NA=is.na(<col_name>),
# <col_name>.fctr=factor(<col_name>,
# as.factor(union(obs_df$<col_name>, obs_twin_df$<col_name>))),
# <col_name>.fctr=relevel(factor(<col_name>,
# as.factor(union(obs_df$<col_name>, obs_twin_df$<col_name>))),
# "<ref_val>"),
# <col2_name>.fctr=relevel(factor(ifelse(<col1_name> == <val>, "<oth_val>", "<ref_val>")),
# as.factor(c("R", "<ref_val>")),
# ref="<ref_val>"),
# This doesn't work - use sapply instead
# <col_name>.fctr_num=grep(<col_name>, levels(<col_name>.fctr)),
#
# Date.my=as.Date(strptime(Date, "%m/%d/%y %H:%M")),
# Year=year(Date.my),
# Month=months(Date.my),
# Weekday=weekdays(Date.my)
# <col_name>.log=log(<col.name>),
# <col_name>=<table>[as.character(<col2_name>)],
# <col_name>=as.numeric(<col2_name>),
.rnorm=rnorm(n=nrow(obs_df))
)
# If levels of a factor are different across obs_df & glb_newent_df; predict.glm fails
# Transformations not handled by mutate
# obs_df$<col_name>.fctr.num <- sapply(1:nrow(obs_df),
# function(row_ix) grep(obs_df[row_ix, "<col_name>"],
# levels(obs_df[row_ix, "<col_name>.fctr"])))
print(summary(obs_df))
print(sapply(names(obs_df), function(col) sum(is.na(obs_df[, col]))))
return(obs_df)
}
glb_entity_df <- add_new_diag_feats(glb_entity_df)## Loading required package: plyr## Population Income Illiteracy Life.Exp
## Min. : 365 Min. :3098 Min. :0.500 Min. :67.96
## 1st Qu.: 1080 1st Qu.:3993 1st Qu.:0.625 1st Qu.:70.12
## Median : 2838 Median :4519 Median :0.950 Median :70.67
## Mean : 4246 Mean :4436 Mean :1.170 Mean :70.88
## 3rd Qu.: 4968 3rd Qu.:4814 3rd Qu.:1.575 3rd Qu.:71.89
## Max. :21198 Max. :6315 Max. :2.800 Max. :73.60
## Murder HS.Grad Frost Area
## Min. : 1.400 Min. :37.80 Min. : 0.00 Min. : 1049
## 1st Qu.: 4.350 1st Qu.:48.05 1st Qu.: 66.25 1st Qu.: 36985
## Median : 6.850 Median :53.25 Median :114.50 Median : 54277
## Mean : 7.378 Mean :53.11 Mean :104.46 Mean : 70736
## 3rd Qu.:10.675 3rd Qu.:59.15 3rd Qu.:139.75 3rd Qu.: 81162
## Max. :15.100 Max. :67.30 Max. :188.00 Max. :566432
## state.abb state.area x y
## Length:50 Min. : 1214 Min. :-127.25 Min. :27.87
## Class :character 1st Qu.: 37317 1st Qu.:-104.16 1st Qu.:35.55
## Mode :character Median : 56222 Median : -89.90 Median :39.62
## Mean : 72368 Mean : -92.46 Mean :39.41
## 3rd Qu.: 83234 3rd Qu.: -78.98 3rd Qu.:43.14
## Max. :589757 Max. : -68.98 Max. :49.25
## state.division state.name state.region
## Length:50 Length:50 Length:50
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
## .rnorm
## Min. :-2.3804
## 1st Qu.:-0.6183
## Median : 0.3611
## Mean : 0.2029
## 3rd Qu.: 0.8468
## Max. : 2.1968
## Population Income Illiteracy Life.Exp Murder
## 0 0 0 0 0
## HS.Grad Frost Area state.abb state.area
## 0 0 0 0 0
## x y state.division state.name state.region
## 0 0 0 0 0
## .rnorm
## 0glb_trnent_df <- add_new_diag_feats(glb_trnent_df)## Population Income Illiteracy Life.Exp
## Min. : 365 Min. :3098 Min. :0.500 Min. :67.96
## 1st Qu.: 1080 1st Qu.:3993 1st Qu.:0.625 1st Qu.:70.12
## Median : 2838 Median :4519 Median :0.950 Median :70.67
## Mean : 4246 Mean :4436 Mean :1.170 Mean :70.88
## 3rd Qu.: 4968 3rd Qu.:4814 3rd Qu.:1.575 3rd Qu.:71.89
## Max. :21198 Max. :6315 Max. :2.800 Max. :73.60
## Murder HS.Grad Frost Area
## Min. : 1.400 Min. :37.80 Min. : 0.00 Min. : 1049
## 1st Qu.: 4.350 1st Qu.:48.05 1st Qu.: 66.25 1st Qu.: 36985
## Median : 6.850 Median :53.25 Median :114.50 Median : 54277
## Mean : 7.378 Mean :53.11 Mean :104.46 Mean : 70736
## 3rd Qu.:10.675 3rd Qu.:59.15 3rd Qu.:139.75 3rd Qu.: 81162
## Max. :15.100 Max. :67.30 Max. :188.00 Max. :566432
## state.abb state.area x y
## Length:50 Min. : 1214 Min. :-127.25 Min. :27.87
## Class :character 1st Qu.: 37317 1st Qu.:-104.16 1st Qu.:35.55
## Mode :character Median : 56222 Median : -89.90 Median :39.62
## Mean : 72368 Mean : -92.46 Mean :39.41
## 3rd Qu.: 83234 3rd Qu.: -78.98 3rd Qu.:43.14
## Max. :589757 Max. : -68.98 Max. :49.25
## state.division state.name state.region
## Length:50 Length:50 Length:50
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
## .rnorm
## Min. :-1.8324
## 1st Qu.:-0.6709
## Median : 0.2725
## Mean : 0.2067
## 3rd Qu.: 0.9290
## Max. : 2.4771
## Population Income Illiteracy Life.Exp Murder
## 0 0 0 0 0
## HS.Grad Frost Area state.abb state.area
## 0 0 0 0 0
## x y state.division state.name state.region
## 0 0 0 0 0
## .rnorm
## 0glb_newent_df <- add_new_diag_feats(glb_newent_df)## Population Income Illiteracy Life.Exp
## Min. : 365 Min. :3098 Min. :0.500 Min. :67.96
## 1st Qu.: 1080 1st Qu.:3993 1st Qu.:0.625 1st Qu.:70.12
## Median : 2838 Median :4519 Median :0.950 Median :70.67
## Mean : 4246 Mean :4436 Mean :1.170 Mean :70.88
## 3rd Qu.: 4968 3rd Qu.:4814 3rd Qu.:1.575 3rd Qu.:71.89
## Max. :21198 Max. :6315 Max. :2.800 Max. :73.60
## Murder HS.Grad Frost Area
## Min. : 1.400 Min. :37.80 Min. : 0.00 Min. : 1049
## 1st Qu.: 4.350 1st Qu.:48.05 1st Qu.: 66.25 1st Qu.: 36985
## Median : 6.850 Median :53.25 Median :114.50 Median : 54277
## Mean : 7.378 Mean :53.11 Mean :104.46 Mean : 70736
## 3rd Qu.:10.675 3rd Qu.:59.15 3rd Qu.:139.75 3rd Qu.: 81162
## Max. :15.100 Max. :67.30 Max. :188.00 Max. :566432
## state.abb state.area x y
## Length:50 Min. : 1214 Min. :-127.25 Min. :27.87
## Class :character 1st Qu.: 37317 1st Qu.:-104.16 1st Qu.:35.55
## Mode :character Median : 56222 Median : -89.90 Median :39.62
## Mean : 72368 Mean : -92.46 Mean :39.41
## 3rd Qu.: 83234 3rd Qu.: -78.98 3rd Qu.:43.14
## Max. :589757 Max. : -68.98 Max. :49.25
## state.division state.name state.region
## Length:50 Length:50 Length:50
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
## .rnorm
## Min. :-2.12355
## 1st Qu.:-0.76752
## Median :-0.01815
## Mean : 0.03124
## 3rd Qu.: 0.80447
## Max. : 2.65579
## Population Income Illiteracy Life.Exp Murder
## 0 0 0 0 0
## HS.Grad Frost Area state.abb state.area
## 0 0 0 0 0
## x y state.division state.name state.region
## 0 0 0 0 0
## .rnorm
## 0# Histogram of predictor in glb_trnent_df & glb_newent_df
plot_df <- rbind(cbind(glb_trnent_df[, glb_rsp_var_raw, FALSE], data.frame(.data="Training")),
cbind(glb_trnent_df[, glb_rsp_var_raw, FALSE], data.frame(.data="New")))
print(myplot_histogram(plot_df, glb_rsp_var_raw) + facet_wrap(~ .data))## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
## stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to adjust this.
if (glb_is_classification) {
xtab_df <- mycreate_xtab(plot_df, c(".data", glb_rsp_var_raw))
rownames(xtab_df) <- xtab_df$.data
xtab_df <- subset(xtab_df, select=-.data)
print(xtab_df / rowSums(xtab_df))
}
# Check for duplicates in glb_id_vars
if (length(glb_id_vars) > 0) {
id_vars_dups_df <- subset(id_vars_df <-
mycreate_tbl_df(glb_entity_df[, glb_id_vars, FALSE], glb_id_vars),
.freq > 1)
if (nrow(id_vars_dups_df) > 0) {
warning("Duplicates found in glb_id_vars data:", nrow(id_vars_dups_df))
myprint_df(id_vars_dups_df)
} else {
# glb_id_vars are unique across obs in both glb_<>_df
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_id_vars)
}
}
#pairs(subset(glb_trnent_df, select=-c(col_symbol)))
# Check for glb_newent_df & glb_trnent_df features range mismatches
# Other diagnostics:
# print(subset(glb_trnent_df, <col1_name> == max(glb_trnent_df$<col1_name>, na.rm=TRUE) &
# <col2_name> <= mean(glb_trnent_df$<col1_name>, na.rm=TRUE)))
# print(glb_trnent_df[which.max(glb_trnent_df$<col_name>),])
# print(<col_name>_freq_glb_trnent_df <- mycreate_tbl_df(glb_trnent_df, "<col_name>"))
# print(which.min(table(glb_trnent_df$<col_name>)))
# print(which.max(table(glb_trnent_df$<col_name>)))
# print(which.max(table(glb_trnent_df$<col1_name>, glb_trnent_df$<col2_name>)[, 2]))
# print(table(glb_trnent_df$<col1_name>, glb_trnent_df$<col2_name>))
# print(table(is.na(glb_trnent_df$<col1_name>), glb_trnent_df$<col2_name>))
# print(table(sign(glb_trnent_df$<col1_name>), glb_trnent_df$<col2_name>))
# print(mycreate_xtab(glb_trnent_df, <col1_name>))
# print(mycreate_xtab(glb_trnent_df, c(<col1_name>, <col2_name>)))
# print(<col1_name>_<col2_name>_xtab_glb_trnent_df <-
# mycreate_xtab(glb_trnent_df, c("<col1_name>", "<col2_name>")))
# <col1_name>_<col2_name>_xtab_glb_trnent_df[is.na(<col1_name>_<col2_name>_xtab_glb_trnent_df)] <- 0
# print(<col1_name>_<col2_name>_xtab_glb_trnent_df <-
# mutate(<col1_name>_<col2_name>_xtab_glb_trnent_df,
# <col3_name>=(<col1_name> * 1.0) / (<col1_name> + <col2_name>)))
# print(<col2_name>_min_entity_arr <-
# sort(tapply(glb_trnent_df$<col1_name>, glb_trnent_df$<col2_name>, min, na.rm=TRUE)))
# print(<col1_name>_na_by_<col2_name>_arr <-
# sort(tapply(glb_trnent_df$<col1_name>.NA, glb_trnent_df$<col2_name>, mean, na.rm=TRUE)))
# Other plots:
# print(myplot_box(df=glb_trnent_df, ycol_names="<col1_name>"))
# print(myplot_box(df=glb_trnent_df, ycol_names="<col1_name>", xcol_name="<col2_name>"))
# print(myplot_line(subset(glb_trnent_df, Symbol %in% c("KO", "PG")),
# "Date.my", "StockPrice", facet_row_colnames="Symbol") +
# geom_vline(xintercept=as.numeric(as.Date("2003-03-01"))) +
# geom_vline(xintercept=as.numeric(as.Date("1983-01-01")))
# )
# print(myplot_scatter(glb_trnent_df, "<col1_name>", "<col2_name>", smooth=TRUE))
print(myplot_scatter(glb_entity_df, "x", "y"))
# print(myplot_scatter(glb_entity_df, "<col1_name>", "<col2_name>", colorcol_name="<Pred.fctr>") +
# geom_point(data=subset(glb_entity_df, <condition>),
# mapping=aes(x=<x_var>, y=<y_var>), color="red", shape=4, size=5))
glb_script_df <- rbind(glb_script_df,
data.frame(chunk_label="manage_missing_data",
chunk_step_major=max(glb_script_df$chunk_step_major),
chunk_step_minor=glb_script_df[nrow(glb_script_df), "chunk_step_minor"]+1,
elapsed=(proc.time() - glb_script_tm)["elapsed"]))
print(tail(glb_script_df, 2))## chunk_label chunk_step_major chunk_step_minor elapsed
## elapsed2 inspectORexplore.data 2 1 0.449
## elapsed3 manage_missing_data 2 2 1.741Step 2.2: manage missing data
# print(sapply(names(glb_trnent_df), function(col) sum(is.na(glb_trnent_df[, col]))))
# print(sapply(names(glb_newent_df), function(col) sum(is.na(glb_newent_df[, col]))))
# glb_trnent_df <- na.omit(glb_trnent_df)
# glb_newent_df <- na.omit(glb_newent_df)
# df[is.na(df)] <- 0
# Not refactored into mydsutils.R since glb_*_df might be reassigned
glb_impute_missing_data <- function(entity_df, newent_df) {
if (!glb_is_separate_newent_dataset) {
# Combine entity & newent
union_df <- rbind(mutate(entity_df, .src = "entity"),
mutate(newent_df, .src = "newent"))
union_imputed_df <- union_df[, setdiff(setdiff(names(entity_df),
glb_rsp_var),
glb_exclude_vars_as_features)]
print(summary(union_imputed_df))
require(mice)
set.seed(glb_mice_complete.seed)
union_imputed_df <- complete(mice(union_imputed_df))
print(summary(union_imputed_df))
union_df[, names(union_imputed_df)] <- union_imputed_df[, names(union_imputed_df)]
print(summary(union_df))
# union_df$.rownames <- rownames(union_df)
# union_df <- orderBy(~.rownames, union_df)
#
# imp_entity_df <- myimport_data(
# url="<imputed_trnng_url>",
# comment="imp_entity_df", force_header=TRUE, print_diagn=TRUE)
# print(all.equal(subset(union_df, select=-c(.src, .rownames, .rnorm)),
# imp_entity_df))
# Partition again
glb_trnent_df <<- subset(union_df, .src == "entity", select=-c(.src, .rownames))
comment(glb_trnent_df) <- "entity_df"
glb_newent_df <<- subset(union_df, .src == "newent", select=-c(.src, .rownames))
comment(glb_newent_df) <- "newent_df"
# Generate summaries
print(summary(entity_df))
print(sapply(names(entity_df), function(col) sum(is.na(entity_df[, col]))))
print(summary(newent_df))
print(sapply(names(newent_df), function(col) sum(is.na(newent_df[, col]))))
} else stop("Not implemented yet")
}
if (glb_impute_na_data) {
if ((sum(sapply(names(glb_trnent_df),
function(col) sum(is.na(glb_trnent_df[, col])))) > 0) |
(sum(sapply(names(glb_newent_df),
function(col) sum(is.na(glb_newent_df[, col])))) > 0))
glb_impute_missing_data(glb_trnent_df, glb_newent_df)
}
glb_script_df <- rbind(glb_script_df,
data.frame(chunk_label="encode_retype_data",
chunk_step_major=max(glb_script_df$chunk_step_major),
chunk_step_minor=glb_script_df[nrow(glb_script_df), "chunk_step_minor"]+1,
elapsed=(proc.time() - glb_script_tm)["elapsed"]))
print(tail(glb_script_df, 2))## chunk_label chunk_step_major chunk_step_minor elapsed
## elapsed3 manage_missing_data 2 2 1.741
## elapsed4 encode_retype_data 2 3 2.164Step 2.3: encode/retype data
# map_<col_name>_df <- myimport_data(
# url="<map_url>",
# comment="map_<col_name>_df", print_diagn=TRUE)
# map_<col_name>_df <- read.csv(paste0(getwd(), "/data/<file_name>.csv"), strip.white=TRUE)
# glb_trnent_df <- mymap_codes(glb_trnent_df, "<from_col_name>", "<to_col_name>",
# map_<to_col_name>_df, map_join_col_name="<map_join_col_name>",
# map_tgt_col_name="<to_col_name>")
# glb_newent_df <- mymap_codes(glb_newent_df, "<from_col_name>", "<to_col_name>",
# map_<to_col_name>_df, map_join_col_name="<map_join_col_name>",
# map_tgt_col_name="<to_col_name>")
# glb_trnent_df$<col_name>.fctr <- factor(glb_trnent_df$<col_name>,
# as.factor(union(glb_trnent_df$<col_name>, glb_newent_df$<col_name>)))
# glb_newent_df$<col_name>.fctr <- factor(glb_newent_df$<col_name>,
# as.factor(union(glb_trnent_df$<col_name>, glb_newent_df$<col_name>)))
if (!is.null(glb_map_rsp_raw_to_var)) {
glb_entity_df[, glb_rsp_var] <-
glb_map_rsp_raw_to_var(glb_entity_df[, glb_rsp_var_raw])
mycheck_map_results(mapd_df=glb_entity_df,
from_col_name=glb_rsp_var_raw, to_col_name=glb_rsp_var)
glb_trnent_df[, glb_rsp_var] <-
glb_map_rsp_raw_to_var(glb_trnent_df[, glb_rsp_var_raw])
mycheck_map_results(mapd_df=glb_trnent_df,
from_col_name=glb_rsp_var_raw, to_col_name=glb_rsp_var)
glb_newent_df[, glb_rsp_var] <-
glb_map_rsp_raw_to_var(glb_newent_df[, glb_rsp_var_raw])
mycheck_map_results(mapd_df=glb_newent_df,
from_col_name=glb_rsp_var_raw, to_col_name=glb_rsp_var)
}
glb_script_df <- rbind(glb_script_df,
data.frame(chunk_label="extract_features",
chunk_step_major=max(glb_script_df$chunk_step_major)+1,
chunk_step_minor=0,
elapsed=(proc.time() - glb_script_tm)["elapsed"]))
print(tail(glb_script_df, 2))## chunk_label chunk_step_major chunk_step_minor elapsed
## elapsed4 encode_retype_data 2 3 2.164
## elapsed5 extract_features 3 0 2.226Step 3: extract features
# Create new features that help prediction
# <col_name>.lag.2 <- lag(zoo(glb_trnent_df$<col_name>), -2, na.pad=TRUE)
# glb_trnent_df[, "<col_name>.lag.2"] <- coredata(<col_name>.lag.2)
# <col_name>.lag.2 <- lag(zoo(glb_newent_df$<col_name>), -2, na.pad=TRUE)
# glb_newent_df[, "<col_name>.lag.2"] <- coredata(<col_name>.lag.2)
#
# glb_newent_df[1, "<col_name>.lag.2"] <- glb_trnent_df[nrow(glb_trnent_df) - 1,
# "<col_name>"]
# glb_newent_df[2, "<col_name>.lag.2"] <- glb_trnent_df[nrow(glb_trnent_df),
# "<col_name>"]
# glb_trnent_df <- mutate(glb_trnent_df,
# <new_col_name>=
# )
# glb_newent_df <- mutate(glb_newent_df,
# <new_col_name>=
# )
# print(summary(glb_trnent_df))
# print(summary(glb_newent_df))
# print(sapply(names(glb_trnent_df), function(col) sum(is.na(glb_trnent_df[, col]))))
# print(sapply(names(glb_newent_df), function(col) sum(is.na(glb_newent_df[, col]))))
# print(myplot_scatter(glb_trnent_df, "<col1_name>", "<col2_name>", smooth=TRUE))
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all","data.new")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
glb_script_df <- rbind(glb_script_df,
data.frame(chunk_label="select_features",
chunk_step_major=max(glb_script_df$chunk_step_major)+1,
chunk_step_minor=0,
elapsed=(proc.time() - glb_script_tm)["elapsed"]))
print(tail(glb_script_df, 2))## chunk_label chunk_step_major chunk_step_minor elapsed
## elapsed5 extract_features 3 0 2.226
## elapsed6 select_features 4 0 3.062Step 4: select features
print(glb_feats_df <- myselect_features(entity_df=glb_trnent_df,
exclude_vars_as_features=glb_exclude_vars_as_features,
rsp_var=glb_rsp_var))## id cor.y exclude.as.feat cor.y.abs
## Murder Murder -0.78084575 0 0.78084575
## Illiteracy Illiteracy -0.58847793 0 0.58847793
## HS.Grad HS.Grad 0.58221620 0 0.58221620
## y y 0.40665458 0 0.40665458
## Income Income 0.34025534 0 0.34025534
## Frost Frost 0.26206801 0 0.26206801
## x x -0.24798347 0 0.24798347
## state.area state.area -0.10963169 1 0.10963169
## Area Area -0.10733194 0 0.10733194
## Population Population -0.06805195 0 0.06805195
## .rnorm .rnorm -0.04828783 0 0.04828783glb_script_df <- rbind(glb_script_df,
data.frame(chunk_label="remove_correlated_features",
chunk_step_major=max(glb_script_df$chunk_step_major),
chunk_step_minor=glb_script_df[nrow(glb_script_df), "chunk_step_minor"]+1,
elapsed=(proc.time() - glb_script_tm)["elapsed"]))
print(tail(glb_script_df, 2))## chunk_label chunk_step_major chunk_step_minor
## elapsed6 select_features 4 0
## elapsed7 remove_correlated_features 4 1
## elapsed
## elapsed6 3.062
## elapsed7 3.272Step 4.1: remove correlated features
print(glb_feats_df <- orderBy(~-cor.y,
myfind_cor_features(feats_df=glb_feats_df, entity_df=glb_trnent_df,
rsp_var=glb_rsp_var)))## Loading required package: reshape2## Murder Illiteracy HS.Grad y Income
## Murder 1.000000000 0.70297520 -0.48797102 -0.65362758 -0.23007761
## Illiteracy 0.702975199 1.00000000 -0.65718861 -0.73282587 -0.43707519
## HS.Grad -0.487971022 -0.65718861 1.00000000 0.50898691 0.61993232
## y -0.653627584 -0.73282587 0.50898691 1.00000000 0.42654579
## Income -0.230077610 -0.43707519 0.61993232 0.42654579 1.00000000
## Frost -0.538883437 -0.67194697 0.36677970 0.71284199 0.22628218
## x -0.009053187 0.09527077 -0.55617620 -0.07900579 -0.23757372
## Area 0.228390211 0.07726113 0.33354187 0.18406742 0.36331544
## Population 0.343642751 0.10762237 -0.09848975 -0.17187501 0.20822756
## .rnorm 0.128017677 -0.03048443 0.09964994 0.06966797 0.09280812
## Frost x Area Population .rnorm
## Murder -0.5388834 -0.009053187 0.22839021 0.34364275 0.12801768
## Illiteracy -0.6719470 0.095270772 0.07726113 0.10762237 -0.03048443
## HS.Grad 0.3667797 -0.556176202 0.33354187 -0.09848975 0.09964994
## y 0.7128420 -0.079005795 0.18406742 -0.17187501 0.06966797
## Income 0.2262822 -0.237573717 0.36331544 0.20822756 0.09280812
## Frost 1.0000000 0.119781966 0.05922910 -0.33215245 0.14609882
## x 0.1197820 1.000000000 -0.58264841 0.15462578 -0.15179543
## Area 0.0592291 -0.582648406 1.00000000 0.02254384 0.24523598
## Population -0.3321525 0.154625784 0.02254384 1.00000000 0.02931449
## .rnorm 0.1460988 -0.151795425 0.24523598 0.02931449 1.00000000
## Murder Illiteracy HS.Grad y Income
## Murder 0.000000000 0.70297520 0.48797102 0.65362758 0.23007761
## Illiteracy 0.702975199 0.00000000 0.65718861 0.73282587 0.43707519
## HS.Grad 0.487971022 0.65718861 0.00000000 0.50898691 0.61993232
## y 0.653627584 0.73282587 0.50898691 0.00000000 0.42654579
## Income 0.230077610 0.43707519 0.61993232 0.42654579 0.00000000
## Frost 0.538883437 0.67194697 0.36677970 0.71284199 0.22628218
## x 0.009053187 0.09527077 0.55617620 0.07900579 0.23757372
## Area 0.228390211 0.07726113 0.33354187 0.18406742 0.36331544
## Population 0.343642751 0.10762237 0.09848975 0.17187501 0.20822756
## .rnorm 0.128017677 0.03048443 0.09964994 0.06966797 0.09280812
## Frost x Area Population .rnorm
## Murder 0.5388834 0.009053187 0.22839021 0.34364275 0.12801768
## Illiteracy 0.6719470 0.095270772 0.07726113 0.10762237 0.03048443
## HS.Grad 0.3667797 0.556176202 0.33354187 0.09848975 0.09964994
## y 0.7128420 0.079005795 0.18406742 0.17187501 0.06966797
## Income 0.2262822 0.237573717 0.36331544 0.20822756 0.09280812
## Frost 0.0000000 0.119781966 0.05922910 0.33215245 0.14609882
## x 0.1197820 0.000000000 0.58264841 0.15462578 0.15179543
## Area 0.0592291 0.582648406 0.00000000 0.02254384 0.24523598
## Population 0.3321525 0.154625784 0.02254384 0.00000000 0.02931449
## .rnorm 0.1460988 0.151795425 0.24523598 0.02931449 0.00000000
## [1] "cor(Illiteracy, y)=-0.7328"
## [1] "cor(Life.Exp, Illiteracy)=-0.5885"
## [1] "cor(Life.Exp, y)=0.4067"## geom_smooth: method="auto" and size of largest group is <1000, so using loess. Use 'method = x' to change the smoothing method.
## geom_smooth: method="auto" and size of largest group is <1000, so using loess. Use 'method = x' to change the smoothing method.## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified y as highly correlated with other features
## [1] "checking correlations for features:"
## [1] "Murder" "Illiteracy" "HS.Grad" "Income" "Frost"
## [6] "x" "Area" "Population" ".rnorm"
## Murder Illiteracy HS.Grad Income Frost
## Murder 1.000000000 0.70297520 -0.48797102 -0.23007761 -0.5388834
## Illiteracy 0.702975199 1.00000000 -0.65718861 -0.43707519 -0.6719470
## HS.Grad -0.487971022 -0.65718861 1.00000000 0.61993232 0.3667797
## Income -0.230077610 -0.43707519 0.61993232 1.00000000 0.2262822
## Frost -0.538883437 -0.67194697 0.36677970 0.22628218 1.0000000
## x -0.009053187 0.09527077 -0.55617620 -0.23757372 0.1197820
## Area 0.228390211 0.07726113 0.33354187 0.36331544 0.0592291
## Population 0.343642751 0.10762237 -0.09848975 0.20822756 -0.3321525
## .rnorm 0.128017677 -0.03048443 0.09964994 0.09280812 0.1460988
## x Area Population .rnorm
## Murder -0.009053187 0.22839021 0.34364275 0.12801768
## Illiteracy 0.095270772 0.07726113 0.10762237 -0.03048443
## HS.Grad -0.556176202 0.33354187 -0.09848975 0.09964994
## Income -0.237573717 0.36331544 0.20822756 0.09280812
## Frost 0.119781966 0.05922910 -0.33215245 0.14609882
## x 1.000000000 -0.58264841 0.15462578 -0.15179543
## Area -0.582648406 1.00000000 0.02254384 0.24523598
## Population 0.154625784 0.02254384 1.00000000 0.02931449
## .rnorm -0.151795425 0.24523598 0.02931449 1.00000000
## Murder Illiteracy HS.Grad Income Frost
## Murder 0.000000000 0.70297520 0.48797102 0.23007761 0.5388834
## Illiteracy 0.702975199 0.00000000 0.65718861 0.43707519 0.6719470
## HS.Grad 0.487971022 0.65718861 0.00000000 0.61993232 0.3667797
## Income 0.230077610 0.43707519 0.61993232 0.00000000 0.2262822
## Frost 0.538883437 0.67194697 0.36677970 0.22628218 0.0000000
## x 0.009053187 0.09527077 0.55617620 0.23757372 0.1197820
## Area 0.228390211 0.07726113 0.33354187 0.36331544 0.0592291
## Population 0.343642751 0.10762237 0.09848975 0.20822756 0.3321525
## .rnorm 0.128017677 0.03048443 0.09964994 0.09280812 0.1460988
## x Area Population .rnorm
## Murder 0.009053187 0.22839021 0.34364275 0.12801768
## Illiteracy 0.095270772 0.07726113 0.10762237 0.03048443
## HS.Grad 0.556176202 0.33354187 0.09848975 0.09964994
## Income 0.237573717 0.36331544 0.20822756 0.09280812
## Frost 0.119781966 0.05922910 0.33215245 0.14609882
## x 0.000000000 0.58264841 0.15462578 0.15179543
## Area 0.582648406 0.00000000 0.02254384 0.24523598
## Population 0.154625784 0.02254384 0.00000000 0.02931449
## .rnorm 0.151795425 0.24523598 0.02931449 0.00000000
## [1] "cor(Murder, Illiteracy)=0.7030"
## [1] "cor(Life.Exp, Murder)=-0.7808"
## [1] "cor(Life.Exp, Illiteracy)=-0.5885"## geom_smooth: method="auto" and size of largest group is <1000, so using loess. Use 'method = x' to change the smoothing method.
## geom_smooth: method="auto" and size of largest group is <1000, so using loess. Use 'method = x' to change the smoothing method.## Warning in myfind_cor_features(feats_df = glb_feats_df, entity_df =
## glb_trnent_df, : Identified Illiteracy as highly correlated with other
## features
## [1] "checking correlations for features:"
## [1] "Murder" "HS.Grad" "Income" "Frost" "x"
## [6] "Area" "Population" ".rnorm"
## Murder HS.Grad Income Frost x
## Murder 1.000000000 -0.48797102 -0.23007761 -0.5388834 -0.009053187
## HS.Grad -0.487971022 1.00000000 0.61993232 0.3667797 -0.556176202
## Income -0.230077610 0.61993232 1.00000000 0.2262822 -0.237573717
## Frost -0.538883437 0.36677970 0.22628218 1.0000000 0.119781966
## x -0.009053187 -0.55617620 -0.23757372 0.1197820 1.000000000
## Area 0.228390211 0.33354187 0.36331544 0.0592291 -0.582648406
## Population 0.343642751 -0.09848975 0.20822756 -0.3321525 0.154625784
## .rnorm 0.128017677 0.09964994 0.09280812 0.1460988 -0.151795425
## Area Population .rnorm
## Murder 0.22839021 0.34364275 0.12801768
## HS.Grad 0.33354187 -0.09848975 0.09964994
## Income 0.36331544 0.20822756 0.09280812
## Frost 0.05922910 -0.33215245 0.14609882
## x -0.58264841 0.15462578 -0.15179543
## Area 1.00000000 0.02254384 0.24523598
## Population 0.02254384 1.00000000 0.02931449
## .rnorm 0.24523598 0.02931449 1.00000000
## Murder HS.Grad Income Frost x
## Murder 0.000000000 0.48797102 0.23007761 0.5388834 0.009053187
## HS.Grad 0.487971022 0.00000000 0.61993232 0.3667797 0.556176202
## Income 0.230077610 0.61993232 0.00000000 0.2262822 0.237573717
## Frost 0.538883437 0.36677970 0.22628218 0.0000000 0.119781966
## x 0.009053187 0.55617620 0.23757372 0.1197820 0.000000000
## Area 0.228390211 0.33354187 0.36331544 0.0592291 0.582648406
## Population 0.343642751 0.09848975 0.20822756 0.3321525 0.154625784
## .rnorm 0.128017677 0.09964994 0.09280812 0.1460988 0.151795425
## Area Population .rnorm
## Murder 0.22839021 0.34364275 0.12801768
## HS.Grad 0.33354187 0.09848975 0.09964994
## Income 0.36331544 0.20822756 0.09280812
## Frost 0.05922910 0.33215245 0.14609882
## x 0.58264841 0.15462578 0.15179543
## Area 0.00000000 0.02254384 0.24523598
## Population 0.02254384 0.00000000 0.02931449
## .rnorm 0.24523598 0.02931449 0.00000000
## id cor.y exclude.as.feat cor.y.abs cor.low
## HS.Grad HS.Grad 0.58221620 0 0.58221620 1
## y y 0.40665458 0 0.40665458 0
## Income Income 0.34025534 0 0.34025534 1
## Frost Frost 0.26206801 0 0.26206801 1
## .rnorm .rnorm -0.04828783 0 0.04828783 1
## Population Population -0.06805195 0 0.06805195 1
## Area Area -0.10733194 0 0.10733194 1
## state.area state.area -0.10963169 1 0.10963169 0
## x x -0.24798347 0 0.24798347 1
## Illiteracy Illiteracy -0.58847793 0 0.58847793 0
## Murder Murder -0.78084575 0 0.78084575 1glb_script_df <- rbind(glb_script_df,
data.frame(chunk_label="fit.models",
chunk_step_major=max(glb_script_df$chunk_step_major)+1,
chunk_step_minor=0,
elapsed=(proc.time() - glb_script_tm)["elapsed"]))
print(tail(glb_script_df, 2))## chunk_label chunk_step_major chunk_step_minor
## elapsed7 remove_correlated_features 4 1
## elapsed8 fit.models 5 0
## elapsed
## elapsed7 3.272
## elapsed8 4.856Step 5: fit models
if (glb_is_classification && glb_is_binomial && (length(unique(glb_trnent_df[, glb_rsp_var])) < 2))
stop("glb_trnent_df$", glb_rsp_var, ": contains less than 2 unique values: ", paste0(unique(glb_trnent_df[, glb_rsp_var]), collapse=", "))
max_cor_y_x_var <- orderBy(~ -cor.y.abs,
subset(glb_feats_df, (exclude.as.feat == 0) & (cor.low == 1)))[1, "id"]
if (!is.null(glb_Baseline_mdl_var)) {
if ((max_cor_y_x_var != glb_Baseline_mdl_var) &
(glb_feats_df[max_cor_y_x_var, "cor.y.abs"] >
glb_feats_df[glb_Baseline_mdl_var, "cor.y.abs"]))
stop(max_cor_y_x_var, " has a lower correlation with ", glb_rsp_var,
" than the Baseline var: ", glb_Baseline_mdl_var)
}
glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
# Any models that have tuning parameters has "better" results with cross-validation (except rf)
# & "different" results for different outcome metrics
# Baseline
if (!is.null(glb_Baseline_mdl_var)) {
# lm_mdl <- lm(reformulate(glb_Baseline_mdl_var,
# response="bucket2009"), data=glb_trnent_df)
# print(summary(lm_mdl))
# plot(lm_mdl, ask=FALSE)
# ret_lst <- myfit_mdl_fn(model_id="Baseline",
# model_method=ifelse(glb_is_regression, "lm",
# ifelse(glb_is_binomial, "glm", "rpart")),
# indep_vars_vctr=glb_Baseline_mdl_var,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_trnent_df, OOB_df=glb_newent_df,
# n_cv_folds=0, tune_models_df=NULL,
# model_loss_mtrx=glb_model_metric_terms,
# model_summaryFunction=glb_model_metric_smmry,
# model_metric=glb_model_metric,
# model_metric_maximize=glb_model_metric_maximize)
ret_lst <- myfit_mdl_fn(model_id="Baseline", model_method="mybaseln_classfr",
indep_vars_vctr=glb_Baseline_mdl_var,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnent_df, OOB_df=glb_newent_df)
}
# Most Frequent Outcome "MFO" model: mean(y) for regression
# Not using caret's nullModel since model stats not avl
# Cannot use rpart for multinomial classification since it predicts non-MFO
ret_lst <- myfit_mdl(model_id="MFO",
model_method=ifelse(glb_is_regression, "lm", "myMFO_classfr"),
model_type=glb_model_type,
indep_vars_vctr=".rnorm",
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnent_df, OOB_df=glb_newent_df)## Loading required package: caret
## Loading required package: lattice
##
## Attaching package: 'caret'
##
## The following object is masked from 'package:survival':
##
## cluster## [1] "fitting model: MFO.lm"
## [1] " indep_vars: .rnorm"
## Fitting parameter = none on full training set
##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9996 -0.7087 -0.1373 1.0939 2.6467
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 70.89089 0.19507 363.415 <2e-16 ***
## .rnorm -0.05945 0.17751 -0.335 0.739
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.355 on 48 degrees of freedom
## Multiple R-squared: 0.002332, Adjusted R-squared: -0.01845
## F-statistic: 0.1122 on 1 and 48 DF, p-value: 0.7391
##
## model_id model_method feats max.nTuningRuns min.elapsedtime.everything
## 1 MFO.lm lm .rnorm 0 0.627
## min.elapsedtime.final max.R.sq.fit min.RMSE.fit max.R.sq.OOB
## 1 0.003 0.002331715 1.327352 -0.0197079
## min.RMSE.OOB max.Adj.R.sq.fit
## 1 1.341933 -0.01845304if (glb_is_classification)
# "random" model - only for classification; none needed for regression since it is same as MFO
ret_lst <- myfit_mdl(model_id="Random", model_method="myrandom_classfr",
model_type=glb_model_type,
indep_vars_vctr=".rnorm",
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnent_df, OOB_df=glb_newent_df)
# Max.cor.Y
# Check impact of cv
# rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
ret_lst <- myfit_mdl(model_id="Max.cor.Y.cv.0",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_var,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnent_df, OOB_df=glb_newent_df)## [1] "fitting model: Max.cor.Y.cv.0.rpart"
## [1] " indep_vars: Murder"## Loading required package: rpart## Fitting cp = 0.479 on full training set## Loading required package: rpart.plot
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 50
##
## CP nsplit rel error
## 1 0.47911 0 1
##
## Node number 1: 50 observations
## mean=70.8786, MSE=1.76598
##
## n= 50
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 50 88.299 70.8786 *
## model_id model_method feats max.nTuningRuns
## 1 Max.cor.Y.cv.0.rpart rpart Murder 0
## min.elapsedtime.everything min.elapsedtime.final max.R.sq.fit
## 1 0.471 0.007 0
## min.RMSE.fit max.R.sq.OOB min.RMSE.OOB
## 1 1.328902 0 1.328902ret_lst <- myfit_mdl(model_id="Max.cor.Y.cv.0.cp.0",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_var,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnent_df, OOB_df=glb_newent_df,
n_cv_folds=0, tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))## [1] "fitting model: Max.cor.Y.cv.0.cp.0.rpart"
## [1] " indep_vars: Murder"
## Fitting cp = 0 on full training set
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 50
##
## CP nsplit rel error
## 1 0.47910997 0 1.0000000
## 2 0.14868323 1 0.5208900
## 3 0.04379453 2 0.3722068
## 4 0.00000000 3 0.3284123
##
## Variable importance
## Murder
## 100
##
## Node number 1: 50 observations, complexity param=0.47911
## mean=70.8786, MSE=1.76598
## left son=2 (27 obs) right son=3 (23 obs)
## Primary splits:
## Murder < 6.55 to the right, improve=0.47911, (0 missing)
##
## Node number 2: 27 observations, complexity param=0.1486832
## mean=70.02963, MSE=0.989848
## left son=4 (8 obs) right son=5 (19 obs)
## Primary splits:
## Murder < 11.2 to the right, improve=0.4912307, (0 missing)
##
## Node number 3: 23 observations, complexity param=0.04379453
## mean=71.87522, MSE=0.8377467
## left son=6 (8 obs) right son=7 (15 obs)
## Primary splits:
## Murder < 4.75 to the right, improve=0.2006943, (0 missing)
##
## Node number 4: 8 observations
## mean=68.955, MSE=0.734025
##
## Node number 5: 19 observations
## mean=70.48211, MSE=0.406585
##
## Node number 6: 8 observations
## mean=71.31375, MSE=1.090298
##
## Node number 7: 15 observations
## mean=72.17467, MSE=0.4452516
##
## n= 50
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 50 88.299000 70.87860
## 2) Murder>=6.55 27 26.725900 70.02963
## 4) Murder>=11.2 8 5.872200 68.95500 *
## 5) Murder< 11.2 19 7.725116 70.48211 *
## 3) Murder< 6.55 23 19.268170 71.87522
## 6) Murder>=4.75 8 8.722387 71.31375 *
## 7) Murder< 4.75 15 6.678773 72.17467 *
## model_id model_method feats max.nTuningRuns
## 1 Max.cor.Y.cv.0.cp.0.rpart rpart Murder 0
## min.elapsedtime.everything min.elapsedtime.final max.R.sq.fit
## 1 0.418 0.005 0.6715877
## min.RMSE.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.7615573 0.6715877 0.7615573if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(model_id="Max.cor.Y",
model_method="rpart",
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_var,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnent_df, OOB_df=glb_newent_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Max.cor.Y.rpart"
## [1] " indep_vars: Murder"
## + Fold1: cp=0.04379
## - Fold1: cp=0.04379
## + Fold2: cp=0.04379
## - Fold2: cp=0.04379
## + Fold3: cp=0.04379
## - Fold3: cp=0.04379## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.149 on full training set
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 50
##
## CP nsplit rel error
## 1 0.4791100 0 1.00000
## 2 0.1486832 1 0.52089
##
## Variable importance
## Murder
## 100
##
## Node number 1: 50 observations, complexity param=0.47911
## mean=70.8786, MSE=1.76598
## left son=2 (27 obs) right son=3 (23 obs)
## Primary splits:
## Murder < 6.55 to the right, improve=0.47911, (0 missing)
##
## Node number 2: 27 observations
## mean=70.02963, MSE=0.989848
##
## Node number 3: 23 observations
## mean=71.87522, MSE=0.8377467
##
## n= 50
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 50 88.29900 70.87860
## 2) Murder>=6.55 27 26.72590 70.02963 *
## 3) Murder< 6.55 23 19.26817 71.87522 *
## model_id model_method feats max.nTuningRuns
## 1 Max.cor.Y.rpart rpart Murder 3
## min.elapsedtime.everything min.elapsedtime.final max.R.sq.fit
## 1 0.8 0.007 0.47911
## min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Rsquared.fit min.RMSESD.fit
## 1 1.039829 0.47911 0.9591045 0.4395211 0.2490712
## max.RsquaredSD.fit
## 1 0.2652752# Used to compare vs. Interactions.High.cor.Y
ret_lst <- myfit_mdl(model_id="Max.cor.Y",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr=max_cor_y_x_var,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnent_df, OOB_df=glb_newent_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Max.cor.Y.lm"
## [1] " indep_vars: Murder"
## + Fold1: parameter=none
## - Fold1: parameter=none
## + Fold2: parameter=none
## - Fold2: parameter=none
## + Fold3: parameter=none
## - Fold3: parameter=none
## Aggregating results
## Fitting final model on full training set
##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.81690 -0.48139 0.09591 0.39769 2.38691
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 72.97356 0.26997 270.30 < 2e-16 ***
## Murder -0.28395 0.03279 -8.66 2.26e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8473 on 48 degrees of freedom
## Multiple R-squared: 0.6097, Adjusted R-squared: 0.6016
## F-statistic: 74.99 on 1 and 48 DF, p-value: 2.26e-11
##
## model_id model_method feats max.nTuningRuns
## 1 Max.cor.Y.lm lm Murder 1
## min.elapsedtime.everything min.elapsedtime.final max.R.sq.fit
## 1 0.706 0.002 0.6097201
## min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.fit max.Rsquared.fit
## 1 0.8320957 0.6097201 0.8301967 0.6015893 0.6384208
## min.RMSESD.fit max.RsquaredSD.fit
## 1 0.3047336 0.264922# Interactions.High.cor.Y
if (nrow(int_feats_df <- subset(glb_feats_df, (cor.low == 0) &
(exclude.as.feat == 0))) > 0) {
# lm & glm handle interaction terms; rpart & rf do not
# This does not work - why ???
# indep_vars_vctr <- ifelse(glb_is_binomial,
# c(max_cor_y_x_var, paste(max_cor_y_x_var,
# subset(glb_feats_df, is.na(cor.low))[, "id"], sep=":")),
# union(max_cor_y_x_var, subset(glb_feats_df, is.na(cor.low))[, "id"]))
if (glb_is_regression || glb_is_binomial) {
indep_vars_vctr <-
c(max_cor_y_x_var, paste(max_cor_y_x_var, int_feats_df[, "id"], sep=":"))
} else { indep_vars_vctr <- union(max_cor_y_x_var, int_feats_df[, "id"]) }
ret_lst <- myfit_mdl(model_id="Interact.High.cor.y",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr,
glb_rsp_var, glb_rsp_var_out,
fit_df=glb_trnent_df, OOB_df=glb_newent_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)
} ## [1] "fitting model: Interact.High.cor.y.lm"
## [1] " indep_vars: Murder, Murder:y, Murder:Illiteracy"
## + Fold1: parameter=none
## - Fold1: parameter=none
## + Fold2: parameter=none
## - Fold2: parameter=none
## + Fold3: parameter=none
## - Fold3: parameter=none
## Aggregating results
## Fitting final model on full training set
##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.73323 -0.53687 0.07993 0.41840 2.33219
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 72.898087 0.309171 235.786 <2e-16 ***
## Murder 0.042494 0.182613 0.233 0.8170
## `Murder:y` -0.006395 0.003975 -1.609 0.1145
## `Murder:Illiteracy` -0.053896 0.031652 -1.703 0.0954 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8348 on 46 degrees of freedom
## Multiple R-squared: 0.6369, Adjusted R-squared: 0.6132
## F-statistic: 26.9 on 3 and 46 DF, p-value: 3.372e-10
##
## model_id model_method feats
## 1 Interact.High.cor.y.lm lm Murder, Murder:y, Murder:Illiteracy
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 0.917 0.003
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.fit
## 1 0.6369065 0.8436601 0.6369065 0.8007596 0.6132265
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.6419329 0.2626833 0.2591354# Low.cor.X
ret_lst <- myfit_mdl(model_id="Low.cor.X",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
indep_vars_vctr=subset(glb_feats_df, cor.low == 1)[, "id"],
model_type=glb_model_type,
glb_rsp_var, glb_rsp_var_out,
fit_df=glb_trnent_df, OOB_df=glb_newent_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL)## [1] "fitting model: Low.cor.X.lm"
## [1] " indep_vars: HS.Grad, Income, Frost, .rnorm, Population, Area, x, Murder"
## + Fold1: parameter=none
## - Fold1: parameter=none
## + Fold2: parameter=none
## - Fold2: parameter=none
## + Fold3: parameter=none
## - Fold3: parameter=none
## Aggregating results
## Fitting final model on full training set
##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.12410 -0.47124 -0.03286 0.52312 1.18588
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.993e+01 1.334e+00 52.433 < 2e-16 ***
## HS.Grad 1.419e-02 2.434e-02 0.583 0.5630
## Income 1.089e-04 2.387e-04 0.456 0.6506
## Frost -3.361e-03 2.835e-03 -1.185 0.2427
## .rnorm 6.152e-02 9.918e-02 0.620 0.5385
## Population 6.798e-05 2.795e-05 2.432 0.0195 *
## Area -2.142e-06 1.757e-06 -1.219 0.2298
## x -2.385e-02 1.091e-02 -2.185 0.0346 *
## Murder -3.103e-01 4.257e-02 -7.289 6.45e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7083 on 41 degrees of freedom
## Multiple R-squared: 0.7671, Adjusted R-squared: 0.7216
## F-statistic: 16.88 on 8 and 41 DF, p-value: 9.511e-11
##
## model_id model_method
## 1 Low.cor.X.lm lm
## feats
## 1 HS.Grad, Income, Frost, .rnorm, Population, Area, x, Murder
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 0.928 0.003
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.fit
## 1 0.7670823 1.084276 0.7662855 0.6424446 0.721635
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.5831535 0.3656232 0.1273796# All X that is not user excluded
model_id_pfx <- "All.X";
indep_vars_vctr <- setdiff(names(glb_trnent_df),
union(glb_rsp_var, glb_exclude_vars_as_features))
for (method in glb_models_method_vctr) {
print(sprintf("iterating over method:%s", method))
ret_lst <- myfit_mdl(model_id=paste0(model_id_pfx, ""), model_method=method,
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnent_df, OOB_df=glb_newent_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df)
# Since caret does not optimize rpart well
if (method == "rpart")
ret_lst <- myfit_mdl(model_id=paste0(model_id_pfx, ".cp.0"), model_method=method,
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnent_df, OOB_df=glb_newent_df,
n_cv_folds=0, tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))
# Compare how rf performs w/i & w/o .rnorm
if (method == "rf")
ret_lst <- myfit_mdl(model_id=paste0(model_id_pfx, ".no.rnorm"), model_method=method,
indep_vars_vctr=setdiff(indep_vars_vctr, c(".rnorm")),
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnent_df, OOB_df=glb_newent_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df)
}## [1] "iterating over method:lm"
## [1] "fitting model: All.X.lm"
## [1] " indep_vars: Population, Income, Illiteracy, Murder, HS.Grad, Frost, Area, x, y, .rnorm"
## + Fold1: parameter=none
## - Fold1: parameter=none
## + Fold2: parameter=none
## - Fold2: parameter=none
## + Fold3: parameter=none
## - Fold3: parameter=none
## Aggregating results
## Fitting final model on full training set
##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.05257 -0.41608 -0.04544 0.51756 1.25603
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.295e+01 2.489e+00 29.306 < 2e-16 ***
## Population 7.709e-05 2.853e-05 2.702 0.0102 *
## Income 1.927e-04 2.357e-04 0.817 0.4187
## Illiteracy -7.134e-02 3.614e-01 -0.197 0.8445
## Murder -3.606e-01 4.837e-02 -7.456 5.11e-09 ***
## HS.Grad 8.650e-03 2.544e-02 0.340 0.7356
## Frost -8.849e-05 3.545e-03 -0.025 0.9802
## Area -9.875e-07 1.961e-06 -0.503 0.6175
## x -2.455e-02 1.074e-02 -2.287 0.0277 *
## y -8.043e-02 3.824e-02 -2.103 0.0419 *
## .rnorm 5.976e-02 9.611e-02 0.622 0.5377
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6863 on 39 degrees of freedom
## Multiple R-squared: 0.792, Adjusted R-squared: 0.7387
## F-statistic: 14.85 on 10 and 39 DF, p-value: 2.028e-10
##
## model_id model_method
## 1 All.X.lm lm
## feats
## 1 Population, Income, Illiteracy, Murder, HS.Grad, Frost, Area, x, y, .rnorm
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 0.71 0.004
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.fit
## 1 0.7919942 1.261888 0.7913361 0.6070389 0.7386593
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.5779784 0.7360377 0.1636766
## [1] "iterating over method:glm"
## [1] "fitting model: All.X.glm"
## [1] " indep_vars: Population, Income, Illiteracy, Murder, HS.Grad, Frost, Area, x, y, .rnorm"
## + Fold1: parameter=none
## - Fold1: parameter=none
## + Fold2: parameter=none
## - Fold2: parameter=none
## + Fold3: parameter=none
## - Fold3: parameter=none
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.05257 -0.41608 -0.04544 0.51756 1.25603
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.295e+01 2.489e+00 29.306 < 2e-16 ***
## Population 7.709e-05 2.853e-05 2.702 0.0102 *
## Income 1.927e-04 2.357e-04 0.817 0.4187
## Illiteracy -7.134e-02 3.614e-01 -0.197 0.8445
## Murder -3.606e-01 4.837e-02 -7.456 5.11e-09 ***
## HS.Grad 8.650e-03 2.544e-02 0.340 0.7356
## Frost -8.849e-05 3.545e-03 -0.025 0.9802
## Area -9.875e-07 1.961e-06 -0.503 0.6175
## x -2.455e-02 1.074e-02 -2.287 0.0277 *
## y -8.043e-02 3.824e-02 -2.103 0.0419 *
## .rnorm 5.976e-02 9.611e-02 0.622 0.5377
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.4709412)
##
## Null deviance: 88.299 on 49 degrees of freedom
## Residual deviance: 18.367 on 39 degrees of freedom
## AIC: 115.82
##
## Number of Fisher Scoring iterations: 2
##
## model_id model_method
## 1 All.X.glm glm
## feats
## 1 Population, Income, Illiteracy, Murder, HS.Grad, Frost, Area, x, y, .rnorm
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 0.732 0.007
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB min.aic.fit
## 1 0.7919942 1.261888 0.7913361 0.6070389 115.8197
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.5779784 0.7360377 0.1636766
## [1] "iterating over method:rpart"
## [1] "fitting model: All.X.rpart"
## [1] " indep_vars: Population, Income, Illiteracy, Murder, HS.Grad, Frost, Area, x, y"
## + Fold1: cp=0.0741
## - Fold1: cp=0.0741
## + Fold2: cp=0.0741
## - Fold2: cp=0.0741
## + Fold3: cp=0.0741
## - Fold3: cp=0.0741## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.149 on full training set
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 50
##
## CP nsplit rel error
## 1 0.4791100 0 1.00000
## 2 0.1486832 1 0.52089
##
## Variable importance
## Murder y HS.Grad Illiteracy Frost Population
## 30 17 14 14 13 11
##
## Node number 1: 50 observations, complexity param=0.47911
## mean=70.8786, MSE=1.76598
## left son=2 (27 obs) right son=3 (23 obs)
## Primary splits:
## Murder < 6.55 to the right, improve=0.4791100, (0 missing)
## HS.Grad < 44.3 to the left, improve=0.4007892, (0 missing)
## Income < 3891 to the left, improve=0.3185349, (0 missing)
## Illiteracy < 1.35 to the right, improve=0.3150196, (0 missing)
## x < -92.94255 to the right, improve=0.2227949, (0 missing)
## Surrogate splits:
## y < 40.56395 to the left, agree=0.80, adj=0.565, (0 split)
## Illiteracy < 1.2 to the right, agree=0.76, adj=0.478, (0 split)
## HS.Grad < 53.25 to the left, agree=0.76, adj=0.478, (0 split)
## Frost < 125.5 to the left, agree=0.74, adj=0.435, (0 split)
## Population < 1671.5 to the right, agree=0.70, adj=0.348, (0 split)
##
## Node number 2: 27 observations
## mean=70.02963, MSE=0.989848
##
## Node number 3: 23 observations
## mean=71.87522, MSE=0.8377467
##
## n= 50
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 50 88.29900 70.87860
## 2) Murder>=6.55 27 26.72590 70.02963 *
## 3) Murder< 6.55 23 19.26817 71.87522 *
## model_id model_method
## 1 All.X.rpart rpart
## feats
## 1 Population, Income, Illiteracy, Murder, HS.Grad, Frost, Area, x, y
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 0.786 0.01
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Rsquared.fit
## 1 0.47911 1.039829 0.47911 0.9591045 0.4395211
## min.RMSESD.fit max.RsquaredSD.fit
## 1 0.2490712 0.2652752
## [1] "fitting model: All.X.cp.0.rpart"
## [1] " indep_vars: Population, Income, Illiteracy, Murder, HS.Grad, Frost, Area, x, y"
## Fitting cp = 0 on full training set## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 50
##
## CP nsplit rel error
## 1 0.47910997 0 1.0000000
## 2 0.14868323 1 0.5208900
## 3 0.07409923 2 0.3722068
## 4 0.00000000 3 0.2981076
##
## Variable importance
## Murder y Illiteracy HS.Grad Frost Population
## 28 16 16 13 11 8
## Area x Income
## 5 3 1
##
## Node number 1: 50 observations, complexity param=0.47911
## mean=70.8786, MSE=1.76598
## left son=2 (27 obs) right son=3 (23 obs)
## Primary splits:
## Murder < 6.55 to the right, improve=0.4791100, (0 missing)
## HS.Grad < 44.3 to the left, improve=0.4007892, (0 missing)
## Income < 3891 to the left, improve=0.3185349, (0 missing)
## Illiteracy < 1.35 to the right, improve=0.3150196, (0 missing)
## x < -92.94255 to the right, improve=0.2227949, (0 missing)
## Surrogate splits:
## y < 40.56395 to the left, agree=0.80, adj=0.565, (0 split)
## Illiteracy < 1.2 to the right, agree=0.76, adj=0.478, (0 split)
## HS.Grad < 53.25 to the left, agree=0.76, adj=0.478, (0 split)
## Frost < 125.5 to the left, agree=0.74, adj=0.435, (0 split)
## Population < 1671.5 to the right, agree=0.70, adj=0.348, (0 split)
##
## Node number 2: 27 observations, complexity param=0.1486832
## mean=70.02963, MSE=0.989848
## left son=4 (8 obs) right son=5 (19 obs)
## Primary splits:
## Murder < 11.2 to the right, improve=0.4912307, (0 missing)
## HS.Grad < 44.8 to the left, improve=0.4129798, (0 missing)
## Income < 4139.5 to the left, improve=0.3715959, (0 missing)
## Illiteracy < 1.95 to the right, improve=0.3131356, (0 missing)
## y < 33.9191 to the left, improve=0.2816457, (0 missing)
## Surrogate splits:
## Illiteracy < 1.95 to the right, agree=0.889, adj=0.625, (0 split)
## y < 33.9191 to the left, agree=0.889, adj=0.625, (0 split)
## HS.Grad < 64.55 to the right, agree=0.778, adj=0.250, (0 split)
## Frost < 62.5 to the left, agree=0.778, adj=0.250, (0 split)
## Area < 209247.5 to the right, agree=0.778, adj=0.250, (0 split)
##
## Node number 3: 23 observations, complexity param=0.07409923
## mean=71.87522, MSE=0.8377467
## left son=6 (9 obs) right son=7 (14 obs)
## Primary splits:
## x < -83.72205 to the right, improve=0.3395697, (0 missing)
## Murder < 4.75 to the right, improve=0.2006943, (0 missing)
## HS.Grad < 59.25 to the left, improve=0.1946401, (0 missing)
## Income < 4458.5 to the left, improve=0.1798387, (0 missing)
## Area < 49715 to the left, improve=0.1668039, (0 missing)
## Surrogate splits:
## Area < 49715 to the left, agree=0.957, adj=0.889, (0 split)
## Illiteracy < 0.65 to the right, agree=0.783, adj=0.444, (0 split)
## HS.Grad < 58.75 to the left, agree=0.783, adj=0.444, (0 split)
## Population < 5201.5 to the right, agree=0.739, adj=0.333, (0 split)
## Income < 3945 to the left, agree=0.696, adj=0.222, (0 split)
##
## Node number 4: 8 observations
## mean=68.955, MSE=0.734025
##
## Node number 5: 19 observations
## mean=70.48211, MSE=0.406585
##
## Node number 6: 9 observations
## mean=71.21, MSE=0.5933778
##
## Node number 7: 14 observations
## mean=72.30286, MSE=0.5274918
##
## n= 50
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 50 88.299000 70.87860
## 2) Murder>=6.55 27 26.725900 70.02963
## 4) Murder>=11.2 8 5.872200 68.95500 *
## 5) Murder< 11.2 19 7.725116 70.48211 *
## 3) Murder< 6.55 23 19.268170 71.87522
## 6) x>=-83.72205 9 5.340400 71.21000 *
## 7) x< -83.72205 14 7.384886 72.30286 *
## model_id model_method
## 1 All.X.cp.0.rpart rpart
## feats
## 1 Population, Income, Illiteracy, Murder, HS.Grad, Frost, Area, x, y
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 0 0.454 0.008
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.7018924 0.7255701 0.7018924 0.7255701
## [1] "iterating over method:rf"
## [1] "fitting model: All.X.rf"
## [1] " indep_vars: Population, Income, Illiteracy, Murder, HS.Grad, Frost, Area, x, y, .rnorm"## Loading required package: randomForest
## randomForest 4.6-10
## Type rfNews() to see new features/changes/bug fixes.
## + : mtry= 2
## - : mtry= 2
## + : mtry= 6
## - : mtry= 6
## + : mtry=10
## - : mtry=10
## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 6 on full training set
## Length Class Mode
## call 4 -none- call
## type 1 -none- character
## predicted 50 -none- numeric
## mse 500 -none- numeric
## rsq 500 -none- numeric
## oob.times 50 -none- numeric
## importance 10 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 11 -none- list
## coefs 0 -none- NULL
## y 50 -none- numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 10 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 1 -none- logical
## model_id model_method
## 1 All.X.rf rf
## feats
## 1 Population, Income, Illiteracy, Murder, HS.Grad, Frost, Area, x, y, .rnorm
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 1.075 0.064
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Rsquared.fit
## 1 0.9240335 0.9176627 0.9151455 0.3871071 0.5231516
## [1] "fitting model: All.X.no.rnorm.rf"
## [1] " indep_vars: Population, Income, Illiteracy, Murder, HS.Grad, Frost, Area, x, y"
## + : mtry=2
## - : mtry=2
## + : mtry=5
## - : mtry=5
## + : mtry=9
## - : mtry=9
## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 2 on full training set## Warning in myfit_mdl(model_id = paste0(model_id_pfx, ".no.rnorm"),
## model_method = method, : model's bestTune found at an extreme of tuneGrid
## for parameter: mtry
## Length Class Mode
## call 4 -none- call
## type 1 -none- character
## predicted 50 -none- numeric
## mse 500 -none- numeric
## rsq 500 -none- numeric
## oob.times 50 -none- numeric
## importance 9 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 11 -none- list
## coefs 0 -none- NULL
## y 50 -none- numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 9 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 1 -none- logical
## model_id model_method
## 1 All.X.no.rnorm.rf rf
## feats
## 1 Population, Income, Illiteracy, Murder, HS.Grad, Frost, Area, x, y
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 0.94 0.037
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Rsquared.fit
## 1 0.9038353 0.9057207 0.9038353 0.4121016 0.5354817# User specified
# easier to exclude features
model_id_pfx <- "Excl.xy.X";
indep_vars_vctr <- setdiff(names(glb_trnent_df),
union(union(glb_rsp_var, glb_exclude_vars_as_features),
c("x", "y")))
for (method in "lm") {
ret_lst <- myfit_mdl(model_id=paste0(model_id_pfx, ""), model_method=method,
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnent_df, OOB_df=glb_newent_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df)
}## [1] "fitting model: Excl.xy.X.lm"
## [1] " indep_vars: Population, Income, Illiteracy, Murder, HS.Grad, Frost, Area, .rnorm"
## + Fold1: parameter=none
## - Fold1: parameter=none
## + Fold2: parameter=none
## - Fold2: parameter=none
## + Fold3: parameter=none
## - Fold3: parameter=none
## Aggregating results
## Fitting final model on full training set
##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.5325 -0.4892 0.0101 0.5755 1.5384
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.108e+01 1.765e+00 40.274 < 2e-16 ***
## Population 5.104e-05 2.934e-05 1.740 0.0894 .
## Income -1.694e-05 2.457e-04 -0.069 0.9454
## Illiteracy 3.389e-02 3.680e-01 0.092 0.9271
## Murder -3.077e-01 4.759e-02 -6.465 9.42e-08 ***
## HS.Grad 4.768e-02 2.349e-02 2.030 0.0489 *
## Frost -6.183e-03 3.210e-03 -1.926 0.0610 .
## Area -2.205e-07 1.687e-06 -0.131 0.8966
## .rnorm 8.134e-02 1.043e-01 0.779 0.4402
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7483 on 41 degrees of freedom
## Multiple R-squared: 0.74, Adjusted R-squared: 0.6893
## F-statistic: 14.59 on 8 and 41 DF, p-value: 8.191e-10
##
## model_id model_method
## 1 Excl.xy.X.lm lm
## feats
## 1 Population, Income, Illiteracy, Murder, HS.Grad, Frost, Area, .rnorm
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 0.736 0.004
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.fit
## 1 0.7400093 0.8718015 0.7331847 0.6864332 0.6892794
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.5965373 0.180015 0.1589779for (method in "rpart") {
ret_lst <- myfit_mdl(model_id=paste0(model_id_pfx, ".cp.0."), model_method=method,
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnent_df, OOB_df=glb_newent_df,
n_cv_folds=0, tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))
ret_lst <- myfit_mdl(model_id=paste0(model_id_pfx, ".cp.opt."), model_method=method,
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnent_df, OOB_df=glb_newent_df,
n_cv_folds=10, tune_models_df=data.frame(parameter="cp", min=0.01, max=0.50, by=0.01))
}## [1] "fitting model: Excl.xy.X.cp.0..rpart"
## [1] " indep_vars: Population, Income, Illiteracy, Murder, HS.Grad, Frost, Area"
## Fitting cp = 0 on full training set## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 50
##
## CP nsplit rel error
## 1 0.47910997 0 1.0000000
## 2 0.14868323 1 0.5208900
## 3 0.04379453 2 0.3722068
## 4 0.00000000 3 0.3284123
##
## Variable importance
## Murder Illiteracy HS.Grad Frost Area Population
## 35 17 14 13 11 10
##
## Node number 1: 50 observations, complexity param=0.47911
## mean=70.8786, MSE=1.76598
## left son=2 (27 obs) right son=3 (23 obs)
## Primary splits:
## Murder < 6.55 to the right, improve=0.4791100, (0 missing)
## HS.Grad < 44.3 to the left, improve=0.4007892, (0 missing)
## Income < 3891 to the left, improve=0.3185349, (0 missing)
## Illiteracy < 1.35 to the right, improve=0.3150196, (0 missing)
## Frost < 102 to the left, improve=0.1559469, (0 missing)
## Surrogate splits:
## Illiteracy < 1.2 to the right, agree=0.76, adj=0.478, (0 split)
## HS.Grad < 53.25 to the left, agree=0.76, adj=0.478, (0 split)
## Frost < 125.5 to the left, agree=0.74, adj=0.435, (0 split)
## Population < 1671.5 to the right, agree=0.70, adj=0.348, (0 split)
## Area < 9579 to the right, agree=0.70, adj=0.348, (0 split)
##
## Node number 2: 27 observations, complexity param=0.1486832
## mean=70.02963, MSE=0.989848
## left son=4 (8 obs) right son=5 (19 obs)
## Primary splits:
## Murder < 11.2 to the right, improve=0.4912307, (0 missing)
## HS.Grad < 44.8 to the left, improve=0.4129798, (0 missing)
## Income < 4139.5 to the left, improve=0.3715959, (0 missing)
## Illiteracy < 1.95 to the right, improve=0.3131356, (0 missing)
## Population < 6859 to the left, improve=0.1953184, (0 missing)
## Surrogate splits:
## Illiteracy < 1.95 to the right, agree=0.889, adj=0.625, (0 split)
## HS.Grad < 64.55 to the right, agree=0.778, adj=0.250, (0 split)
## Frost < 62.5 to the left, agree=0.778, adj=0.250, (0 split)
## Area < 209247.5 to the right, agree=0.778, adj=0.250, (0 split)
## Population < 867 to the left, agree=0.741, adj=0.125, (0 split)
##
## Node number 3: 23 observations, complexity param=0.04379453
## mean=71.87522, MSE=0.8377467
## left son=6 (8 obs) right son=7 (15 obs)
## Primary splits:
## Murder < 4.75 to the right, improve=0.20069430, (0 missing)
## HS.Grad < 59.25 to the left, improve=0.19464010, (0 missing)
## Income < 4458.5 to the left, improve=0.17983870, (0 missing)
## Area < 49715 to the left, improve=0.16680390, (0 missing)
## Population < 840.5 to the left, improve=0.09003604, (0 missing)
## Surrogate splits:
## Population < 608 to the left, agree=0.739, adj=0.250, (0 split)
## Illiteracy < 0.85 to the right, agree=0.739, adj=0.250, (0 split)
## Frost < 126.5 to the left, agree=0.739, adj=0.250, (0 split)
## Income < 4002.5 to the left, agree=0.696, adj=0.125, (0 split)
## HS.Grad < 52.9 to the left, agree=0.696, adj=0.125, (0 split)
##
## Node number 4: 8 observations
## mean=68.955, MSE=0.734025
##
## Node number 5: 19 observations
## mean=70.48211, MSE=0.406585
##
## Node number 6: 8 observations
## mean=71.31375, MSE=1.090298
##
## Node number 7: 15 observations
## mean=72.17467, MSE=0.4452516
##
## n= 50
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 50 88.299000 70.87860
## 2) Murder>=6.55 27 26.725900 70.02963
## 4) Murder>=11.2 8 5.872200 68.95500 *
## 5) Murder< 11.2 19 7.725116 70.48211 *
## 3) Murder< 6.55 23 19.268170 71.87522
## 6) Murder>=4.75 8 8.722387 71.31375 *
## 7) Murder< 4.75 15 6.678773 72.17467 *
## model_id model_method
## 1 Excl.xy.X.cp.0..rpart rpart
## feats
## 1 Population, Income, Illiteracy, Murder, HS.Grad, Frost, Area
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 0 0.461 0.007
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.6715877 0.7615573 0.6715877 0.7615573
## [1] "fitting model: Excl.xy.X.cp.opt..rpart"
## [1] " indep_vars: Population, Income, Illiteracy, Murder, HS.Grad, Frost, Area"
## + Fold01: cp=0.01
## - Fold01: cp=0.01
## + Fold02: cp=0.01
## - Fold02: cp=0.01
## + Fold03: cp=0.01
## - Fold03: cp=0.01
## + Fold04: cp=0.01
## - Fold04: cp=0.01
## + Fold05: cp=0.01
## - Fold05: cp=0.01
## + Fold06: cp=0.01
## - Fold06: cp=0.01
## + Fold07: cp=0.01
## - Fold07: cp=0.01
## + Fold08: cp=0.01
## - Fold08: cp=0.01
## + Fold09: cp=0.01
## - Fold09: cp=0.01
## + Fold10: cp=0.01
## - Fold10: cp=0.01## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.12 on full training set
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 50
##
## CP nsplit rel error
## 1 0.4791100 0 1.0000000
## 2 0.1486832 1 0.5208900
## 3 0.1200000 2 0.3722068
##
## Variable importance
## Murder Illiteracy HS.Grad Frost Area Population
## 34 17 14 13 11 10
##
## Node number 1: 50 observations, complexity param=0.47911
## mean=70.8786, MSE=1.76598
## left son=2 (27 obs) right son=3 (23 obs)
## Primary splits:
## Murder < 6.55 to the right, improve=0.4791100, (0 missing)
## HS.Grad < 44.3 to the left, improve=0.4007892, (0 missing)
## Income < 3891 to the left, improve=0.3185349, (0 missing)
## Illiteracy < 1.35 to the right, improve=0.3150196, (0 missing)
## Frost < 102 to the left, improve=0.1559469, (0 missing)
## Surrogate splits:
## Illiteracy < 1.2 to the right, agree=0.76, adj=0.478, (0 split)
## HS.Grad < 53.25 to the left, agree=0.76, adj=0.478, (0 split)
## Frost < 125.5 to the left, agree=0.74, adj=0.435, (0 split)
## Population < 1671.5 to the right, agree=0.70, adj=0.348, (0 split)
## Area < 9579 to the right, agree=0.70, adj=0.348, (0 split)
##
## Node number 2: 27 observations, complexity param=0.1486832
## mean=70.02963, MSE=0.989848
## left son=4 (8 obs) right son=5 (19 obs)
## Primary splits:
## Murder < 11.2 to the right, improve=0.4912307, (0 missing)
## HS.Grad < 44.8 to the left, improve=0.4129798, (0 missing)
## Income < 4139.5 to the left, improve=0.3715959, (0 missing)
## Illiteracy < 1.95 to the right, improve=0.3131356, (0 missing)
## Population < 6859 to the left, improve=0.1953184, (0 missing)
## Surrogate splits:
## Illiteracy < 1.95 to the right, agree=0.889, adj=0.625, (0 split)
## HS.Grad < 64.55 to the right, agree=0.778, adj=0.250, (0 split)
## Frost < 62.5 to the left, agree=0.778, adj=0.250, (0 split)
## Area < 209247.5 to the right, agree=0.778, adj=0.250, (0 split)
## Population < 867 to the left, agree=0.741, adj=0.125, (0 split)
##
## Node number 3: 23 observations
## mean=71.87522, MSE=0.8377467
##
## Node number 4: 8 observations
## mean=68.955, MSE=0.734025
##
## Node number 5: 19 observations
## mean=70.48211, MSE=0.406585
##
## n= 50
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 50 88.299000 70.87860
## 2) Murder>=6.55 27 26.725900 70.02963
## 4) Murder>=11.2 8 5.872200 68.95500 *
## 5) Murder< 11.2 19 7.725116 70.48211 *
## 3) Murder< 6.55 23 19.268170 71.87522 *
## model_id model_method
## 1 Excl.xy.X.cp.opt..rpart rpart
## feats
## 1 Population, Income, Illiteracy, Murder, HS.Grad, Frost, Area
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 50 3.038 0.008
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Rsquared.fit
## 1 0.6277932 0.9709231 0.6277932 0.8107464 0.5380553
## min.RMSESD.fit max.RsquaredSD.fit
## 1 0.3042443 0.2994809# Non-caret models
rpart_excl_xy_mdl <- rpart(reformulate(indep_vars_vctr, response=glb_rsp_var),
data=glb_trnent_df, #method="class",
control=rpart.control(cp=0.12))
print("rpart_excl_xy_mdl"); prp(rpart_excl_xy_mdl)## [1] "rpart_excl_xy_mdl"
# easier to include features
model_id_pfx <- "Imp.4.X";
indep_vars_vctr <- c("Population", "Murder", "Frost", "HS.Grad")
for (method in "lm") {
print(sprintf("iterating over method:%s", method))
ret_lst <- myfit_mdl(model_id=paste0(model_id_pfx, ""), model_method=method,
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnent_df, OOB_df=glb_newent_df,
n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df)
}## [1] "iterating over method:lm"
## [1] "fitting model: Imp.4.X.lm"
## [1] " indep_vars: Population, Murder, Frost, HS.Grad"
## + Fold1: parameter=none
## - Fold1: parameter=none
## + Fold2: parameter=none
## - Fold2: parameter=none
## + Fold3: parameter=none
## - Fold3: parameter=none
## Aggregating results
## Fitting final model on full training set
##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.47095 -0.53464 -0.03701 0.57621 1.50683
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.103e+01 9.529e-01 74.542 < 2e-16 ***
## Population 5.014e-05 2.512e-05 1.996 0.05201 .
## Murder -3.001e-01 3.661e-02 -8.199 1.77e-10 ***
## Frost -5.943e-03 2.421e-03 -2.455 0.01802 *
## HS.Grad 4.658e-02 1.483e-02 3.142 0.00297 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7197 on 45 degrees of freedom
## Multiple R-squared: 0.736, Adjusted R-squared: 0.7126
## F-statistic: 31.37 on 4 and 45 DF, p-value: 1.696e-12
##
## model_id model_method feats
## 1 Imp.4.X.lm lm Population, Murder, Frost, HS.Grad
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 0.736 0.003
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.fit
## 1 0.7360328 0.7842579 0.7360328 0.6827597 0.712569
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.6595997 0.1797279 0.1316844model_id_pfx <- "Area.X";
indep_vars_vctr <- c("Area")
for (method in "rpart") {
print(sprintf("iterating over method:%s", method))
ret_lst <- myfit_mdl(model_id=paste0(model_id_pfx, ""), model_method=method,
indep_vars_vctr=indep_vars_vctr,
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnent_df, OOB_df=glb_newent_df,
n_cv_folds=10, tune_models_df=data.frame(parameter="cp", min=0.01, max=0.50, by=0.01))
}## [1] "iterating over method:rpart"
## [1] "fitting model: Area.X.rpart"
## [1] " indep_vars: Area"
## + Fold01: cp=0.01
## - Fold01: cp=0.01
## + Fold02: cp=0.01
## - Fold02: cp=0.01
## + Fold03: cp=0.01
## - Fold03: cp=0.01
## + Fold04: cp=0.01
## - Fold04: cp=0.01
## + Fold05: cp=0.01
## - Fold05: cp=0.01
## + Fold06: cp=0.01
## - Fold06: cp=0.01
## + Fold07: cp=0.01
## - Fold07: cp=0.01
## + Fold08: cp=0.01
## - Fold08: cp=0.01
## + Fold09: cp=0.01
## - Fold09: cp=0.01
## + Fold10: cp=0.01
## - Fold10: cp=0.01## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.02 on full training set
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 50
##
## CP nsplit rel error
## 1 0.14566773 0 1.0000000
## 2 0.06165287 3 0.5629968
## 3 0.00000000 4 0.5013439
##
## Variable importance
## Area
## 100
##
## Node number 1: 50 observations, complexity param=0.1456677
## mean=70.8786, MSE=1.76598
## left son=2 (30 obs) right son=3 (20 obs)
## Primary splits:
## Area < 62321.5 to the left, improve=0.1118817, (0 missing)
##
## Node number 2: 30 observations, complexity param=0.1456677
## mean=70.51567, MSE=1.753125
## left son=4 (22 obs) right son=5 (8 obs)
## Primary splits:
## Area < 9579 to the right, improve=0.2952542, (0 missing)
##
## Node number 3: 20 observations, complexity param=0.1456677
## mean=71.423, MSE=1.291311
## left son=6 (9 obs) right son=7 (11 obs)
## Primary splits:
## Area < 96693.5 to the right, improve=0.51031, (0 missing)
##
## Node number 4: 22 observations, complexity param=0.06165287
## mean=70.08182, MSE=1.331069
## left son=8 (15 obs) right son=9 (7 obs)
## Primary splits:
## Area < 51326.5 to the left, improve=0.1859027, (0 missing)
##
## Node number 5: 8 observations
## mean=71.70875, MSE=0.9727109
##
## Node number 6: 9 observations
## mean=70.52556, MSE=0.8568247
##
## Node number 7: 11 observations
## mean=72.15727, MSE=0.4486744
##
## Node number 8: 15 observations
## mean=69.742, MSE=0.8206027
##
## Node number 9: 7 observations
## mean=70.81, MSE=1.647229
##
## n= 50
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 50 88.299000 70.87860
## 2) Area< 62321.5 30 52.593740 70.51567
## 4) Area>=9579 22 29.283530 70.08182
## 8) Area< 51326.5 15 12.309040 69.74200 *
## 9) Area>=51326.5 7 11.530600 70.81000 *
## 5) Area< 9579 8 7.781687 71.70875 *
## 3) Area>=62321.5 20 25.826220 71.42300
## 6) Area>=96693.5 9 7.711422 70.52556 *
## 7) Area< 96693.5 11 4.935418 72.15727 *
## model_id model_method feats max.nTuningRuns
## 1 Area.X.rpart rpart Area 50
## min.elapsedtime.everything min.elapsedtime.final max.R.sq.fit
## 1 2.465 0.005 0.4986561
## min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Rsquared.fit min.RMSESD.fit
## 1 1.205723 0.4986561 0.9409375 0.5389506 0.4437874
## max.RsquaredSD.fit
## 1 0.3188591 rpart_area_mdl <- rpart(reformulate("Area", response=glb_rsp_var),
data=glb_trnent_df, #method="class",
control=rpart.control(cp=0.12))
print("rpart_excl_xy_mdl"); prp(rpart_excl_xy_mdl)## [1] "rpart_excl_xy_mdl"
# User specified bivariate models
# indep_vars_vctr_lst <- list()
# for (feat in setdiff(names(glb_trnent_df),
# union(glb_rsp_var, glb_exclude_vars_as_features)))
# indep_vars_vctr_lst[["feat"]] <- feat
# User specified combinatorial models
# indep_vars_vctr_lst <- list()
# combn_mtrx <- combn(c("<feat1_name>", "<feat2_name>", "<featn_name>"),
# <num_feats_to_choose>)
# for (combn_ix in 1:ncol(combn_mtrx))
# #print(combn_mtrx[, combn_ix])
# indep_vars_vctr_lst[[combn_ix]] <- combn_mtrx[, combn_ix]
# rf is hard-coded in caret to recognize only Accuracy / Kappa evaluation metrics
# only for OOB in trainControl ?
# template for myfit_mdl
# ret_lst <- myfit_mdl_fn(model_id=paste0(model_id_pfx, ""), model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_trnent_df, OOB_df=glb_newent_df,
# n_cv_folds=glb_n_cv_folds, tune_models_df=glb_tune_models_df,
# model_loss_mtrx=glb_model_metric_terms,
# model_summaryFunction=glb_model_metric_smmry,
# model_metric=glb_model_metric,
# model_metric_maximize=glb_model_metric_maximize)
# Simplify a model
# fit_df <- glb_trnent_df; glb_mdl <- step(<complex>_mdl)
# Non-caret models
# glb_sel_mdl <- glb_sel_wlm_mdl <- ret_lst[["model"]]
# rpart_sel_wlm_mdl <- rpart(reformulate(indep_vars_vctr, response=glb_rsp_var),
# data=glb_trnent_df, method="class",
# control=rpart.control(cp=glb_sel_wlm_mdl$bestTune$cp),
# parms=list(loss=glb_model_metric_terms))
# print("rpart_sel_wlm_mdl"); prp(rpart_sel_wlm_mdl)
#
print(glb_models_df)## model_id model_method
## 1 MFO.lm lm
## 2 Max.cor.Y.cv.0.rpart rpart
## 3 Max.cor.Y.cv.0.cp.0.rpart rpart
## 4 Max.cor.Y.rpart rpart
## 5 Max.cor.Y.lm lm
## 6 Interact.High.cor.y.lm lm
## 7 Low.cor.X.lm lm
## 8 All.X.lm lm
## 9 All.X.glm glm
## 10 All.X.rpart rpart
## 11 All.X.cp.0.rpart rpart
## 12 All.X.rf rf
## 13 All.X.no.rnorm.rf rf
## 14 Excl.xy.X.lm lm
## 15 Excl.xy.X.cp.0..rpart rpart
## 16 Excl.xy.X.cp.opt..rpart rpart
## 17 Imp.4.X.lm lm
## 18 Area.X.rpart rpart
## feats
## 1 .rnorm
## 2 Murder
## 3 Murder
## 4 Murder
## 5 Murder
## 6 Murder, Murder:y, Murder:Illiteracy
## 7 HS.Grad, Income, Frost, .rnorm, Population, Area, x, Murder
## 8 Population, Income, Illiteracy, Murder, HS.Grad, Frost, Area, x, y, .rnorm
## 9 Population, Income, Illiteracy, Murder, HS.Grad, Frost, Area, x, y, .rnorm
## 10 Population, Income, Illiteracy, Murder, HS.Grad, Frost, Area, x, y
## 11 Population, Income, Illiteracy, Murder, HS.Grad, Frost, Area, x, y
## 12 Population, Income, Illiteracy, Murder, HS.Grad, Frost, Area, x, y, .rnorm
## 13 Population, Income, Illiteracy, Murder, HS.Grad, Frost, Area, x, y
## 14 Population, Income, Illiteracy, Murder, HS.Grad, Frost, Area, .rnorm
## 15 Population, Income, Illiteracy, Murder, HS.Grad, Frost, Area
## 16 Population, Income, Illiteracy, Murder, HS.Grad, Frost, Area
## 17 Population, Murder, Frost, HS.Grad
## 18 Area
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 0 0.627 0.003
## 2 0 0.471 0.007
## 3 0 0.418 0.005
## 4 3 0.800 0.007
## 5 1 0.706 0.002
## 6 1 0.917 0.003
## 7 1 0.928 0.003
## 8 1 0.710 0.004
## 9 1 0.732 0.007
## 10 3 0.786 0.010
## 11 0 0.454 0.008
## 12 3 1.075 0.064
## 13 3 0.940 0.037
## 14 1 0.736 0.004
## 15 0 0.461 0.007
## 16 50 3.038 0.008
## 17 1 0.736 0.003
## 18 50 2.465 0.005
## max.R.sq.fit min.RMSE.fit max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.fit
## 1 0.002331715 1.3273516 -0.0197079 1.3419329 -0.01845304
## 2 0.000000000 1.3289018 0.0000000 1.3289018 NA
## 3 0.671587719 0.7615573 0.6715877 0.7615573 NA
## 4 0.479109965 1.0398286 0.4791100 0.9591045 NA
## 5 0.609720089 0.8320957 0.6097201 0.8301967 0.60158926
## 6 0.636906500 0.8436601 0.6369065 0.8007596 0.61322649
## 7 0.767082328 1.0842757 0.7662855 0.6424446 0.72163498
## 8 0.791994171 1.2618881 0.7913361 0.6070389 0.73865934
## 9 0.791994171 1.2618881 0.7913361 0.6070389 NA
## 10 0.479109965 1.0398286 0.4791100 0.9591045 NA
## 11 0.701892423 0.7255701 0.7018924 0.7255701 NA
## 12 0.924033485 0.9176627 0.9151455 0.3871071 NA
## 13 0.903835274 0.9057207 0.9038353 0.4121016 NA
## 14 0.740009310 0.8718015 0.7331847 0.6864332 0.68927942
## 15 0.671587719 0.7615573 0.6715877 0.7615573 NA
## 16 0.627793192 0.9709231 0.6277932 0.8107464 NA
## 17 0.736032772 0.7842579 0.7360328 0.6827597 0.71256902
## 18 0.498656079 1.2057226 0.4986561 0.9409375 NA
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit min.aic.fit
## 1 NA NA NA NA
## 2 NA NA NA NA
## 3 NA NA NA NA
## 4 0.4395211 0.2490712 0.2652752 NA
## 5 0.6384208 0.3047336 0.2649220 NA
## 6 0.6419329 0.2626833 0.2591354 NA
## 7 0.5831535 0.3656232 0.1273796 NA
## 8 0.5779784 0.7360377 0.1636766 NA
## 9 0.5779784 0.7360377 0.1636766 115.8197
## 10 0.4395211 0.2490712 0.2652752 NA
## 11 NA NA NA NA
## 12 0.5231516 NA NA NA
## 13 0.5354817 NA NA NA
## 14 0.5965373 0.1800150 0.1589779 NA
## 15 NA NA NA NA
## 16 0.5380553 0.3042443 0.2994809 NA
## 17 0.6595997 0.1797279 0.1316844 NA
## 18 0.5389506 0.4437874 0.3188591 NAglb_script_df <- rbind(glb_script_df,
data.frame(chunk_label="fit.models",
chunk_step_major=glb_script_df[nrow(glb_script_df), "chunk_step_major"],
chunk_step_minor=glb_script_df[nrow(glb_script_df), "chunk_step_minor"]+1,
elapsed=(proc.time() - glb_script_tm)["elapsed"]))
print(tail(glb_script_df, 2))## chunk_label chunk_step_major chunk_step_minor elapsed
## elapsed8 fit.models 5 0 4.856
## elapsed9 fit.models 5 1 46.815if (!is.null(glb_model_metric_smmry)) {
stats_df <- glb_models_df[, "model_id", FALSE]
stats_mdl_df <- data.frame()
for (model_id in stats_df$model_id) {
stats_mdl_df <- rbind(stats_mdl_df,
mypredict_mdl(glb_models_lst[[model_id]], glb_trnent_df, glb_rsp_var,
glb_rsp_var_out, model_id, "fit",
glb_model_metric_smmry, glb_model_metric,
glb_model_metric_maximize, ret_type="stats"))
}
stats_df <- merge(stats_df, stats_mdl_df, all.x=TRUE)
stats_mdl_df <- data.frame()
for (model_id in stats_df$model_id) {
stats_mdl_df <- rbind(stats_mdl_df,
mypredict_mdl(glb_models_lst[[model_id]], glb_newent_df, glb_rsp_var,
glb_rsp_var_out, model_id, "OOB",
glb_model_metric_smmry, glb_model_metric,
glb_model_metric_maximize, ret_type="stats"))
}
stats_df <- merge(stats_df, stats_mdl_df, all.x=TRUE)
# tmp_models_df <- orderBy(~model_id, glb_models_df)
# rownames(tmp_models_df) <- seq(1, nrow(tmp_models_df))
# all.equal(subset(tmp_models_df[, names(stats_df)], model_id != "Random.myrandom_classfr"),
# subset(stats_df, model_id != "Random.myrandom_classfr"))
# print(subset(tmp_models_df[, names(stats_df)], model_id != "Random.myrandom_classfr")[, c("model_id", "max.Accuracy.fit")])
# print(subset(stats_df, model_id != "Random.myrandom_classfr")[, c("model_id", "max.Accuracy.fit")])
print("Merging following data into glb_models_df:")
print(stats_mrg_df <- stats_df[, c(1, grep(glb_model_metric, names(stats_df)))])
print(tmp_models_df <- orderBy(~model_id, glb_models_df[, c("model_id", grep(glb_model_metric, names(stats_df), value=TRUE))]))
tmp2_models_df <- glb_models_df[, c("model_id", setdiff(names(glb_models_df), grep(glb_model_metric, names(stats_df), value=TRUE)))]
tmp3_models_df <- merge(tmp2_models_df, stats_mrg_df, all.x=TRUE, sort=FALSE)
print(tmp3_models_df)
print(names(tmp3_models_df))
print(glb_models_df <- subset(tmp3_models_df, select=-model_id.1))
}
plt_models_df <- glb_models_df[, -grep("SD|Upper|Lower", names(glb_models_df))]
for (var in grep("^min.", names(plt_models_df), value=TRUE)) {
plt_models_df[, sub("min.", "inv.", var)] <-
#ifelse(all(is.na(tmp <- plt_models_df[, var])), NA, 1.0 / tmp)
1.0 / plt_models_df[, var]
plt_models_df <- plt_models_df[ , -grep(var, names(plt_models_df))]
}
print(plt_models_df)## model_id model_method
## 1 MFO.lm lm
## 2 Max.cor.Y.cv.0.rpart rpart
## 3 Max.cor.Y.cv.0.cp.0.rpart rpart
## 4 Max.cor.Y.rpart rpart
## 5 Max.cor.Y.lm lm
## 6 Interact.High.cor.y.lm lm
## 7 Low.cor.X.lm lm
## 8 All.X.lm lm
## 9 All.X.glm glm
## 10 All.X.rpart rpart
## 11 All.X.cp.0.rpart rpart
## 12 All.X.rf rf
## 13 All.X.no.rnorm.rf rf
## 14 Excl.xy.X.lm lm
## 15 Excl.xy.X.cp.0..rpart rpart
## 16 Excl.xy.X.cp.opt..rpart rpart
## 17 Imp.4.X.lm lm
## 18 Area.X.rpart rpart
## feats
## 1 .rnorm
## 2 Murder
## 3 Murder
## 4 Murder
## 5 Murder
## 6 Murder, Murder:y, Murder:Illiteracy
## 7 HS.Grad, Income, Frost, .rnorm, Population, Area, x, Murder
## 8 Population, Income, Illiteracy, Murder, HS.Grad, Frost, Area, x, y, .rnorm
## 9 Population, Income, Illiteracy, Murder, HS.Grad, Frost, Area, x, y, .rnorm
## 10 Population, Income, Illiteracy, Murder, HS.Grad, Frost, Area, x, y
## 11 Population, Income, Illiteracy, Murder, HS.Grad, Frost, Area, x, y
## 12 Population, Income, Illiteracy, Murder, HS.Grad, Frost, Area, x, y, .rnorm
## 13 Population, Income, Illiteracy, Murder, HS.Grad, Frost, Area, x, y
## 14 Population, Income, Illiteracy, Murder, HS.Grad, Frost, Area, .rnorm
## 15 Population, Income, Illiteracy, Murder, HS.Grad, Frost, Area
## 16 Population, Income, Illiteracy, Murder, HS.Grad, Frost, Area
## 17 Population, Murder, Frost, HS.Grad
## 18 Area
## max.nTuningRuns max.R.sq.fit max.R.sq.OOB max.Adj.R.sq.fit
## 1 0 0.002331715 -0.0197079 -0.01845304
## 2 0 0.000000000 0.0000000 NA
## 3 0 0.671587719 0.6715877 NA
## 4 3 0.479109965 0.4791100 NA
## 5 1 0.609720089 0.6097201 0.60158926
## 6 1 0.636906500 0.6369065 0.61322649
## 7 1 0.767082328 0.7662855 0.72163498
## 8 1 0.791994171 0.7913361 0.73865934
## 9 1 0.791994171 0.7913361 NA
## 10 3 0.479109965 0.4791100 NA
## 11 0 0.701892423 0.7018924 NA
## 12 3 0.924033485 0.9151455 NA
## 13 3 0.903835274 0.9038353 NA
## 14 1 0.740009310 0.7331847 0.68927942
## 15 0 0.671587719 0.6715877 NA
## 16 50 0.627793192 0.6277932 NA
## 17 1 0.736032772 0.7360328 0.71256902
## 18 50 0.498656079 0.4986561 NA
## max.Rsquared.fit inv.elapsedtime.everything inv.elapsedtime.final
## 1 NA 1.5948963 333.33333
## 2 NA 2.1231423 142.85714
## 3 NA 2.3923445 200.00000
## 4 0.4395211 1.2500000 142.85714
## 5 0.6384208 1.4164306 500.00000
## 6 0.6419329 1.0905125 333.33333
## 7 0.5831535 1.0775862 333.33333
## 8 0.5779784 1.4084507 250.00000
## 9 0.5779784 1.3661202 142.85714
## 10 0.4395211 1.2722646 100.00000
## 11 NA 2.2026432 125.00000
## 12 0.5231516 0.9302326 15.62500
## 13 0.5354817 1.0638298 27.02703
## 14 0.5965373 1.3586957 250.00000
## 15 NA 2.1691974 142.85714
## 16 0.5380553 0.3291639 125.00000
## 17 0.6595997 1.3586957 333.33333
## 18 0.5389506 0.4056795 200.00000
## inv.RMSE.fit inv.RMSE.OOB inv.aic.fit
## 1 0.7533799 0.7451938 NA
## 2 0.7525010 0.7525010 NA
## 3 1.3130988 1.3130988 NA
## 4 0.9616969 1.0426393 NA
## 5 1.2017848 1.2045338 NA
## 6 1.1853115 1.2488143 NA
## 7 0.9222747 1.5565544 NA
## 8 0.7924633 1.6473410 NA
## 9 0.7924633 1.6473410 0.008634111
## 10 0.9616969 1.0426393 NA
## 11 1.3782265 1.3782265 NA
## 12 1.0897250 2.5832642 NA
## 13 1.1040931 2.4265858 NA
## 14 1.1470501 1.4568060 NA
## 15 1.3130988 1.3130988 NA
## 16 1.0299477 1.2334313 NA
## 17 1.2750907 1.4646441 NA
## 18 0.8293782 1.0627699 NAprint(myplot_radar(radar_inp_df=plt_models_df))## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 18. Consider specifying shapes manually. if you must have them.## Warning: Removed 8 rows containing missing values (geom_path).## Warning: Removed 131 rows containing missing values (geom_point).## Warning: Removed 33 rows containing missing values (geom_text).## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 18. Consider specifying shapes manually. if you must have them.
# print(myplot_radar(radar_inp_df=subset(plt_models_df,
# !(model_id %in% grep("random|MFO", plt_models_df$model_id, value=TRUE)))))
# Compute CI for <metric>SD
glb_models_df <- mutate(glb_models_df,
max.df = ifelse(max.nTuningRuns > 1, max.nTuningRuns - 1, NA),
min.sd2ci.scaler = ifelse(is.na(max.df), NA, qt(0.975, max.df)))
for (var in grep("SD", names(glb_models_df), value=TRUE)) {
# Does CI alredy exist ?
var_components <- unlist(strsplit(var, "SD"))
varActul <- paste0(var_components[1], var_components[2])
varUpper <- paste0(var_components[1], "Upper", var_components[2])
varLower <- paste0(var_components[1], "Lower", var_components[2])
if (varUpper %in% names(glb_models_df)) {
warning(varUpper, " already exists in glb_models_df")
# Assuming Lower also exists
next
}
print(sprintf("var:%s", var))
# CI is dependent on sample size in t distribution; df=n-1
glb_models_df[, varUpper] <- glb_models_df[, varActul] +
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
glb_models_df[, varLower] <- glb_models_df[, varActul] -
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
}## [1] "var:min.RMSESD.fit"
## [1] "var:max.RsquaredSD.fit"# Plot metrics with CI
plt_models_df <- glb_models_df[, "model_id", FALSE]
pltCI_models_df <- glb_models_df[, "model_id", FALSE]
for (var in grep("Upper", names(glb_models_df), value=TRUE)) {
var_components <- unlist(strsplit(var, "Upper"))
col_name <- unlist(paste(var_components, collapse=""))
plt_models_df[, col_name] <- glb_models_df[, col_name]
for (name in paste0(var_components[1], c("Upper", "Lower"), var_components[2]))
pltCI_models_df[, name] <- glb_models_df[, name]
}
build_statsCI_data <- function(plt_models_df) {
mltd_models_df <- melt(plt_models_df, id.vars="model_id")
mltd_models_df$data <- sapply(1:nrow(mltd_models_df),
function(row_ix) tail(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]), "[.]")), 1))
mltd_models_df$label <- sapply(1:nrow(mltd_models_df),
function(row_ix) head(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]), paste0(".", mltd_models_df[row_ix, "data"]))), 1))
#print(mltd_models_df)
return(mltd_models_df)
}
mltd_models_df <- build_statsCI_data(plt_models_df)
mltdCI_models_df <- melt(pltCI_models_df, id.vars="model_id")
for (row_ix in 1:nrow(mltdCI_models_df)) {
for (type in c("Upper", "Lower")) {
if (length(var_components <- unlist(strsplit(
as.character(mltdCI_models_df[row_ix, "variable"]), type))) > 1) {
#print(sprintf("row_ix:%d; type:%s; ", row_ix, type))
mltdCI_models_df[row_ix, "label"] <- var_components[1]
mltdCI_models_df[row_ix, "data"] <- unlist(strsplit(var_components[2], "[.]"))[2]
mltdCI_models_df[row_ix, "type"] <- type
break
}
}
}
#print(mltdCI_models_df)
# castCI_models_df <- dcast(mltdCI_models_df, value ~ type, fun.aggregate=sum)
# print(castCI_models_df)
wideCI_models_df <- reshape(subset(mltdCI_models_df, select=-variable),
timevar="type",
idvar=setdiff(names(mltdCI_models_df), c("type", "value", "variable")),
direction="wide")
#print(wideCI_models_df)
mrgdCI_models_df <- merge(wideCI_models_df, mltd_models_df, all.x=TRUE)
#print(mrgdCI_models_df)
# Merge stats back in if CIs don't exist
goback_vars <- c()
for (var in unique(mltd_models_df$label)) {
for (type in unique(mltd_models_df$data)) {
var_type <- paste0(var, ".", type)
# if this data is already present, next
if (var_type %in% unique(paste(mltd_models_df$label, mltd_models_df$data, sep=".")))
next
#print(sprintf("var_type:%s", var_type))
goback_vars <- c(goback_vars, var_type)
}
}
if (length(goback_vars) > 0) {
mltd_goback_df <- build_statsCI_data(glb_models_df[, c("model_id", goback_vars)])
mltd_models_df <- rbind(mltd_models_df, mltd_goback_df)
}
mltd_models_df <- merge(mltd_models_df, glb_models_df[, c("model_id", "model_method")], all.x=TRUE)
png(paste0(glb_out_pfx, "models_bar.png"), width=480*3, height=480*2)
print(gp <- myplot_bar(mltd_models_df, "model_id", "value", colorcol_name="model_method") +
geom_errorbar(data=mrgdCI_models_df,
mapping=aes(x=model_id, ymax=value.Upper, ymin=value.Lower), width=0.5) +
facet_grid(label ~ data, scales="free") +
theme(axis.text.x = element_text(angle = 90,vjust = 0.5)))## Warning: Removed 5 rows containing missing values (position_stack).dev.off()## quartz_off_screen
## 2print(gp)## Warning: Removed 5 rows containing missing values (position_stack).
# used for console inspection
model_evl_terms <- c(NULL)
for (metric in glb_model_evl_criteria)
model_evl_terms <- c(model_evl_terms,
ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse=" "))
print(tmp_models_df <- orderBy(model_sel_frmla, glb_models_df)[, c("model_id", glb_model_evl_criteria)])## model_id min.RMSE.OOB max.R.sq.OOB max.Adj.R.sq.fit
## 12 All.X.rf 0.3871071 0.9151455 NA
## 13 All.X.no.rnorm.rf 0.4121016 0.9038353 NA
## 8 All.X.lm 0.6070389 0.7913361 0.73865934
## 9 All.X.glm 0.6070389 0.7913361 NA
## 7 Low.cor.X.lm 0.6424446 0.7662855 0.72163498
## 17 Imp.4.X.lm 0.6827597 0.7360328 0.71256902
## 14 Excl.xy.X.lm 0.6864332 0.7331847 0.68927942
## 11 All.X.cp.0.rpart 0.7255701 0.7018924 NA
## 3 Max.cor.Y.cv.0.cp.0.rpart 0.7615573 0.6715877 NA
## 15 Excl.xy.X.cp.0..rpart 0.7615573 0.6715877 NA
## 6 Interact.High.cor.y.lm 0.8007596 0.6369065 0.61322649
## 16 Excl.xy.X.cp.opt..rpart 0.8107464 0.6277932 NA
## 5 Max.cor.Y.lm 0.8301967 0.6097201 0.60158926
## 18 Area.X.rpart 0.9409375 0.4986561 NA
## 4 Max.cor.Y.rpart 0.9591045 0.4791100 NA
## 10 All.X.rpart 0.9591045 0.4791100 NA
## 2 Max.cor.Y.cv.0.rpart 1.3289018 0.0000000 NA
## 1 MFO.lm 1.3419329 -0.0197079 -0.01845304print(myplot_radar(radar_inp_df=tmp_models_df))## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 18. Consider specifying shapes manually. if you must have them.## Warning: Removed 8 rows containing missing values (geom_path).## Warning: Removed 47 rows containing missing values (geom_point).## Warning: Removed 11 rows containing missing values (geom_text).## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 18. Consider specifying shapes manually. if you must have them.
print("Metrics used for model selection:"); print(model_sel_frmla)## [1] "Metrics used for model selection:"## ~+min.RMSE.OOB - max.R.sq.OOB - max.Adj.R.sq.fitprint(sprintf("Best model id: %s", tmp_models_df[1, "model_id"]))## [1] "Best model id: All.X.rf"if (is.null(glb_sel_mdl_id))
{ glb_sel_mdl_id <- tmp_models_df[1, "model_id"] } else
print(sprintf("User specified selection: %s", glb_sel_mdl_id))
myprint_mdl(glb_sel_mdl <- glb_models_lst[[glb_sel_mdl_id]])
## Length Class Mode
## call 4 -none- call
## type 1 -none- character
## predicted 50 -none- numeric
## mse 500 -none- numeric
## rsq 500 -none- numeric
## oob.times 50 -none- numeric
## importance 10 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 11 -none- list
## coefs 0 -none- NULL
## y 50 -none- numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 10 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 1 -none- logical## [1] TRUEreplay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"model.selected")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
glb_script_df <- rbind(glb_script_df,
data.frame(chunk_label="fit.data.training.all",
chunk_step_major=max(glb_script_df$chunk_step_major)+1,
chunk_step_minor=0,
elapsed=(proc.time() - glb_script_tm)["elapsed"]))
print(tail(glb_script_df, 2))## chunk_label chunk_step_major chunk_step_minor elapsed
## elapsed9 fit.models 5 1 46.815
## elapsed10 fit.data.training.all 6 0 57.490Step 6: fit.data.training.all
if (!is.null(glb_fin_mdl_id) && (glb_fin_mdl_id %in% names(glb_models_lst))) {
warning("Final model same as user selected model")
glb_fin_mdl <- glb_sel_mdl
} else {
print(mdl_feats_df <- myextract_mdl_feats(sel_mdl=glb_sel_mdl, entity_df=glb_trnent_df))
if ((model_method <- glb_sel_mdl$method) == "custom")
# get actual method from the model_id
model_method <- tail(unlist(strsplit(glb_sel_mdl_id, "[.]")), 1)
# Sync with parameters in mydsutils.R
ret_lst <- myfit_mdl(model_id="Final", model_method=model_method,
indep_vars_vctr=mdl_feats_df$id, model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnent_df, OOB_df=NULL,
# Automate from here
# Issues if glb_sel_mdl$method == "rf" b/c trainControl is "oob"; not "cv"
n_cv_folds=glb_n_cv_folds, tune_models_df=NULL,
model_loss_mtrx=glb_model_metric_terms,
model_summaryFunction=glb_sel_mdl$control$summaryFunction,
model_metric=glb_sel_mdl$metric,
model_metric_maximize=glb_sel_mdl$maximize)
glb_fin_mdl <- glb_models_lst[[length(glb_models_lst)]]
glb_fin_mdl_id <- glb_models_df[length(glb_models_lst), "model_id"]
}## importance id fit.feat
## Murder 100.000000 Murder TRUE
## HS.Grad 40.289971 HS.Grad TRUE
## Illiteracy 17.851797 Illiteracy TRUE
## x 13.913356 x TRUE
## Income 13.849496 Income TRUE
## Population 9.779550 Population TRUE
## y 9.125939 y TRUE
## Area 8.055817 Area TRUE
## Frost 7.562218 Frost TRUE
## .rnorm 4.619481 .rnorm TRUE
## [1] "fitting model: Final.rf"
## [1] " indep_vars: Murder, HS.Grad, Illiteracy, x, Income, Population, y, Area, Frost, .rnorm"
## + : mtry= 2
## - : mtry= 2
## + : mtry= 6
## - : mtry= 6
## + : mtry=10
## - : mtry=10
## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 6 on full training set
## Length Class Mode
## call 4 -none- call
## type 1 -none- character
## predicted 50 -none- numeric
## mse 500 -none- numeric
## rsq 500 -none- numeric
## oob.times 50 -none- numeric
## importance 10 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 11 -none- list
## coefs 0 -none- NULL
## y 50 -none- numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 10 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 1 -none- logical
## model_id model_method
## 1 Final.rf rf
## feats
## 1 Murder, HS.Grad, Illiteracy, x, Income, Population, y, Area, Frost, .rnorm
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 3 1.019 0.064
## max.R.sq.fit min.RMSE.fit max.Rsquared.fit
## 1 0.9219111 0.9077502 0.5333977glb_script_df <- rbind(glb_script_df,
data.frame(chunk_label="fit.data.training.all",
chunk_step_major=glb_script_df[nrow(glb_script_df), "chunk_step_major"],
chunk_step_minor=glb_script_df[nrow(glb_script_df), "chunk_step_minor"]+1,
elapsed=(proc.time() - glb_script_tm)["elapsed"]))
print(tail(glb_script_df, 2))## chunk_label chunk_step_major chunk_step_minor elapsed
## elapsed10 fit.data.training.all 6 0 57.490
## elapsed11 fit.data.training.all 6 1 61.469glb_rsp_var_out <- paste0(glb_rsp_var_out, tail(names(glb_models_lst), 1))
# Used again in predict.data.new chunk
glb_get_predictions <- function(df) {
if (glb_is_regression) {
df[, glb_rsp_var_out] <- predict(glb_fin_mdl, newdata=df, type="raw")
print(myplot_scatter(df, glb_rsp_var, glb_rsp_var_out,
smooth=TRUE))
df[, paste0(glb_rsp_var_out, ".err")] <-
abs(df[, glb_rsp_var_out] - df[, glb_rsp_var])
print(head(orderBy(reformulate(c("-", paste0(glb_rsp_var_out, ".err"))),
df)))
}
if (glb_is_classification && glb_is_binomial) {
# incorporate glb_clf_proba_threshold
# shd it only be for glb_fin_mdl or for earlier models ?
if (glb_models_df[glb_models_df$model_id == glb_fin_mdl_id, "opt.prob.threshold.fit"] !=
glb_models_df[glb_models_df$model_id == glb_sel_mdl_id, "opt.prob.threshold.fit"])
stop("user specification for probability threshold required")
else prob_threshold <-
glb_models_df[glb_models_df$model_id == glb_fin_mdl_id, "opt.prob.threshold.OOB"]
df[, paste0(glb_rsp_var_out, ".prob")] <-
predict(glb_fin_mdl, newdata=df, type="prob")[, 2]
df[, glb_rsp_var_out] <-
factor(levels(df[, glb_rsp_var])[
(df[, paste0(glb_rsp_var_out, ".prob")] >=
prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
# prediction stats already reported by myfit_mdl ???
}
if (glb_is_classification && !glb_is_binomial) {
df[, glb_rsp_var_out] <- predict(glb_fin_mdl, newdata=df, type="raw")
}
return(df)
}
glb_trnent_df <- glb_get_predictions(glb_trnent_df)## geom_smooth: method="auto" and size of largest group is <1000, so using loess. Use 'method = x' to change the smoothing method.
## Population Income Illiteracy Life.Exp Murder HS.Grad Frost Area
## 11 868 4963 1.9 73.60 6.2 61.9 0 6425
## 28 590 5149 0.5 69.03 11.5 65.2 188 109889
## 6 2541 4884 0.7 72.06 6.8 63.9 166 103766
## 40 2816 3635 2.3 67.96 11.6 37.8 65 30225
## 8 579 4809 0.9 70.06 6.2 54.6 103 1982
## 26 746 4347 0.6 70.56 5.0 59.2 155 145587
## state.abb state.area x y state.division state.name
## 11 HI 6450 -126.2500 31.7500 Pacific Hawaii
## 28 NV 110540 -116.8510 39.1063 Mountain Nevada
## 6 CO 104247 -105.5130 38.6777 Mountain Colorado
## 40 SC 31055 -80.5056 33.6190 South Atlantic South Carolina
## 8 DE 2057 -74.9841 38.6777 South Atlantic Delaware
## 26 MT 147138 -109.3200 46.8230 Mountain Montana
## state.region .rnorm Life.Exp.predict.Final.rf
## 11 West -1.0493528 72.50644
## 28 West 0.8248701 69.76368
## 6 West 1.5934885 71.39975
## 40 South -1.1562233 68.58484
## 8 South -1.2956717 70.64891
## 26 West -0.3079534 71.14370
## Life.Exp.predict.Final.rf.err
## 11 1.0935600
## 28 0.7336800
## 6 0.6602510
## 40 0.6248437
## 8 0.5889147
## 26 0.5836993print(glb_feats_df <- mymerge_feats_importance(feats_df=glb_feats_df, sel_mdl=glb_fin_mdl,
entity_df=glb_trnent_df))## id cor.y exclude.as.feat cor.y.abs cor.low importance
## 7 Murder -0.78084575 0 0.78084575 1 100.000000
## 4 HS.Grad 0.58221620 0 0.58221620 1 35.649279
## 5 Illiteracy -0.58847793 0 0.58847793 0 22.392070
## 6 Income 0.34025534 0 0.34025534 1 14.589273
## 10 x -0.24798347 0 0.24798347 1 13.975075
## 2 Area -0.10733194 0 0.10733194 1 9.376642
## 8 Population -0.06805195 0 0.06805195 1 9.171128
## 3 Frost 0.26206801 0 0.26206801 1 7.354350
## 11 y 0.40665458 0 0.40665458 0 7.296889
## 1 .rnorm -0.04828783 0 0.04828783 1 4.414972
## 9 state.area -0.10963169 1 0.10963169 0 NA# Used again in predict.data.new chunk
glb_analytics_diag_plots <- function(obs_df) {
for (var in subset(glb_feats_df, !is.na(importance))$id) {
plot_df <- melt(obs_df, id.vars=var,
measure.vars=c(glb_rsp_var, glb_rsp_var_out))
# if (var == "<feat_name>") print(myplot_scatter(plot_df, var, "value",
# facet_colcol_name="variable") +
# geom_vline(xintercept=<divider_val>, linetype="dotted")) else
print(myplot_scatter(plot_df, var, "value", colorcol_name="variable",
facet_colcol_name="variable", jitter=TRUE) +
guides(color=FALSE))
}
if (glb_is_regression) {
# plot_vars_df <- subset(glb_feats_df, importance >
# glb_feats_df[glb_feats_df$id == ".rnorm", "importance"])
plot_vars_df <- orderBy(~ -importance, glb_feats_df)
if (nrow(plot_vars_df) == 0)
warning("No important features in glb_fin_mdl") else
print(myplot_prediction_regression(df=obs_df,
feat_x=ifelse(nrow(plot_vars_df) > 1, plot_vars_df$id[2],
".rownames"),
feat_y=plot_vars_df$id[1],
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
id_vars=glb_id_vars)
# + facet_wrap(reformulate(plot_vars_df$id[2])) # if [1 or 2] is a factor
# + geom_point(aes_string(color="<col_name>.fctr")) # to color the plot
)
}
if (glb_is_classification) {
if (nrow(plot_vars_df <- subset(glb_feats_df, !is.na(importance))) == 0)
warning("No features in selected model are statistically important")
else print(myplot_prediction_classification(df=obs_df,
feat_x=ifelse(nrow(plot_vars_df) > 1, plot_vars_df$id[2],
".rownames"),
feat_y=plot_vars_df$id[1],
rsp_var=glb_rsp_var,
rsp_var_out=glb_rsp_var_out,
id_vars=glb_id_vars)
# + geom_hline(yintercept=<divider_val>, linetype = "dotted")
)
}
}
glb_analytics_diag_plots(obs_df=glb_trnent_df)
## Population Income Illiteracy Life.Exp Murder HS.Grad Frost Area
## 11 868 4963 1.9 73.60 6.2 61.9 0 6425
## 28 590 5149 0.5 69.03 11.5 65.2 188 109889
## 6 2541 4884 0.7 72.06 6.8 63.9 166 103766
## 40 2816 3635 2.3 67.96 11.6 37.8 65 30225
## 8 579 4809 0.9 70.06 6.2 54.6 103 1982
## state.abb state.area x y state.division state.name
## 11 HI 6450 -126.2500 31.7500 Pacific Hawaii
## 28 NV 110540 -116.8510 39.1063 Mountain Nevada
## 6 CO 104247 -105.5130 38.6777 Mountain Colorado
## 40 SC 31055 -80.5056 33.6190 South Atlantic South Carolina
## 8 DE 2057 -74.9841 38.6777 South Atlantic Delaware
## state.region .rnorm Life.Exp.predict.Final.rf
## 11 West -1.0493528 72.50644
## 28 West 0.8248701 69.76368
## 6 West 1.5934885 71.39975
## 40 South -1.1562233 68.58484
## 8 South -1.2956717 70.64891
## Life.Exp.predict.Final.rf.err .label
## 11 1.0935600 HI
## 28 0.7336800 NV
## 6 0.6602510 CO
## 40 0.6248437 SC
## 8 0.5889147 DE
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all.prediction","model.final")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
## 3.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: data.training.all.prediction
## 4.0000 5 0 1 1 1
## 4.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: model.final
## 5.0000 4 0 0 2 1
glb_script_df <- rbind(glb_script_df,
data.frame(chunk_label="predict.data.new",
chunk_step_major=max(glb_script_df$chunk_step_major)+1,
chunk_step_minor=0,
elapsed=(proc.time() - glb_script_tm)["elapsed"]))
print(tail(glb_script_df, 2))## chunk_label chunk_step_major chunk_step_minor elapsed
## elapsed11 fit.data.training.all 6 1 61.469
## elapsed12 predict.data.new 7 0 67.388Step 7: predict data.new
# Compute final model predictions
glb_newent_df <- glb_get_predictions(glb_newent_df)## geom_smooth: method="auto" and size of largest group is <1000, so using loess. Use 'method = x' to change the smoothing method.
## Population Income Illiteracy Life.Exp Murder HS.Grad Frost Area
## 11 868 4963 1.9 73.60 6.2 61.9 0 6425
## 28 590 5149 0.5 69.03 11.5 65.2 188 109889
## 8 579 4809 0.9 70.06 6.2 54.6 103 1982
## 6 2541 4884 0.7 72.06 6.8 63.9 166 103766
## 40 2816 3635 2.3 67.96 11.6 37.8 65 30225
## 26 746 4347 0.6 70.56 5.0 59.2 155 145587
## state.abb state.area x y state.division state.name
## 11 HI 6450 -126.2500 31.7500 Pacific Hawaii
## 28 NV 110540 -116.8510 39.1063 Mountain Nevada
## 8 DE 2057 -74.9841 38.6777 South Atlantic Delaware
## 6 CO 104247 -105.5130 38.6777 Mountain Colorado
## 40 SC 31055 -80.5056 33.6190 South Atlantic South Carolina
## 26 MT 147138 -109.3200 46.8230 Mountain Montana
## state.region .rnorm Life.Exp.predict.Final.rf
## 11 West 0.32215158 72.42603
## 28 West -0.42257687 69.77368
## 8 South -0.06901731 70.74923
## 6 West 0.62886063 71.37722
## 40 South 0.54839792 68.60615
## 26 West 0.66892165 71.18499
## Life.Exp.predict.Final.rf.err
## 11 1.1739723
## 28 0.7436837
## 8 0.6892330
## 6 0.6827823
## 40 0.6461463
## 26 0.6249920glb_analytics_diag_plots(obs_df=glb_newent_df)
## Population Income Illiteracy Life.Exp Murder HS.Grad Frost Area
## 11 868 4963 1.9 73.60 6.2 61.9 0 6425
## 28 590 5149 0.5 69.03 11.5 65.2 188 109889
## 8 579 4809 0.9 70.06 6.2 54.6 103 1982
## 6 2541 4884 0.7 72.06 6.8 63.9 166 103766
## 40 2816 3635 2.3 67.96 11.6 37.8 65 30225
## state.abb state.area x y state.division state.name
## 11 HI 6450 -126.2500 31.7500 Pacific Hawaii
## 28 NV 110540 -116.8510 39.1063 Mountain Nevada
## 8 DE 2057 -74.9841 38.6777 South Atlantic Delaware
## 6 CO 104247 -105.5130 38.6777 Mountain Colorado
## 40 SC 31055 -80.5056 33.6190 South Atlantic South Carolina
## state.region .rnorm Life.Exp.predict.Final.rf
## 11 West 0.32215158 72.42603
## 28 West -0.42257687 69.77368
## 8 South -0.06901731 70.74923
## 6 West 0.62886063 71.37722
## 40 South 0.54839792 68.60615
## Life.Exp.predict.Final.rf.err .label
## 11 1.1739723 HI
## 28 0.7436837 NV
## 8 0.6892330 DE
## 6 0.6827823 CO
## 40 0.6461463 SC
tmp_replay_lst <- replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.new.prediction")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
## 3.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: data.training.all.prediction
## 4.0000 5 0 1 1 1
## 4.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: model.final
## 5.0000 4 0 0 2 1
## 6.0000 6 0 0 1 2
print(ggplot.petrinet(tmp_replay_lst[["pn"]]) + coord_flip())
Null Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.
## chunk_label chunk_step_major chunk_step_minor elapsed
## 10 fit.models 5 1 46.815
## 11 fit.data.training.all 6 0 57.490
## 13 predict.data.new 7 0 67.388
## 12 fit.data.training.all 6 1 61.469
## 9 fit.models 5 0 4.856
## 4 manage_missing_data 2 2 1.741
## 7 select_features 4 0 3.062
## 5 encode_retype_data 2 3 2.164
## 2 cleanse_data 2 0 0.413
## 8 remove_correlated_features 4 1 3.272
## 6 extract_features 3 0 2.226
## 3 inspectORexplore.data 2 1 0.449
## 1 import_data 1 0 0.002
## elapsed_diff
## 10 41.959
## 11 10.675
## 13 5.919
## 12 3.979
## 9 1.584
## 4 1.292
## 7 0.836
## 5 0.423
## 2 0.411
## 8 0.210
## 6 0.062
## 3 0.036
## 1 0.000## [1] "Total Elapsed Time: 67.388 secs"
## R version 3.1.3 (2015-03-09)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: OS X 10.10.3 (Yosemite)
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] randomForest_4.6-10 rpart.plot_1.5.2 rpart_4.1-9
## [4] caret_6.0-41 lattice_0.20-31 reshape2_1.4.1
## [7] plyr_1.8.1 doBy_4.5-13 survival_2.38-1
## [10] ggplot2_1.0.1
##
## loaded via a namespace (and not attached):
## [1] BradleyTerry2_1.0-6 brglm_0.5-9 car_2.0-25
## [4] codetools_0.2-11 colorspace_1.2-6 compiler_3.1.3
## [7] digest_0.6.8 evaluate_0.5.5 foreach_1.4.2
## [10] formatR_1.1 gtable_0.1.2 gtools_3.4.1
## [13] htmltools_0.2.6 iterators_1.0.7 knitr_1.9
## [16] labeling_0.3 lme4_1.1-7 MASS_7.3-40
## [19] Matrix_1.2-0 mgcv_1.8-6 minqa_1.2.4
## [22] munsell_0.4.2 nlme_3.1-120 nloptr_1.0.4
## [25] nnet_7.3-9 parallel_3.1.3 pbkrtest_0.4-2
## [28] proto_0.3-10 quantreg_5.11 RColorBrewer_1.1-2
## [31] Rcpp_0.11.5 rmarkdown_0.5.1 scales_0.2.4
## [34] SparseM_1.6 splines_3.1.3 stringr_0.6.2
## [37] tools_3.1.3 yaml_2.1.13